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21 Research Objectives Examples (Copy and Paste)

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Research objectives refer to the definitive statements made by researchers at the beginning of a research project detailing exactly what a research project aims to achieve.

These objectives are explicit goals clearly and concisely projected by the researcher to present a clear intention or course of action for his or her qualitative or quantitative study. 

Research objectives are typically nested under one overarching research aim. The objectives are the steps you’ll need to take in order to achieve the aim (see the examples below, for example, which demonstrate an aim followed by 3 objectives, which is what I recommend to my research students).

Research Objectives vs Research Aims

Research aim and research objectives are fundamental constituents of any study, fitting together like two pieces of the same puzzle.

The ‘research aim’ describes the overarching goal or purpose of the study (Kumar, 2019). This is usually a broad, high-level purpose statement, summing up the central question that the research intends to answer.

Example of an Overarching Research Aim:

“The aim of this study is to explore the impact of climate change on crop productivity.” 

Comparatively, ‘research objectives’ are concrete goals that underpin the research aim, providing stepwise actions to achieve the aim.

Objectives break the primary aim into manageable, focused pieces, and are usually characterized as being more specific, measurable, achievable, relevant, and time-bound (SMART).

Examples of Specific Research Objectives:

1. “To examine the effects of rising temperatures on the yield of rice crops during the upcoming growth season.” 2. “To assess changes in rainfall patterns in major agricultural regions over the first decade of the twenty-first century (2000-2010).” 3. “To analyze the impact of changing weather patterns on crop diseases within the same timeframe.”

The distinction between these two terms, though subtle, is significant for successfully conducting a study. The research aim provides the study with direction, while the research objectives set the path to achieving this aim, thereby ensuring the study’s efficiency and effectiveness.

How to Write Research Objectives

I usually recommend to my students that they use the SMART framework to create their research objectives.

SMART is an acronym standing for Specific, Measurable, Achievable, Relevant, and Time-bound. It provides a clear method of defining solid research objectives and helps students know where to start in writing their objectives (Locke & Latham, 2013).

Each element of this acronym adds a distinct dimension to the framework, aiding in the creation of comprehensive, well-delineated objectives.

Here is each step:

• Specific : We need to avoid ambiguity in our objectives. They need to be clear and precise (Doran, 1981). For instance, rather than stating the objective as “to study the effects of social media,” a more focused detail would be “to examine the effects of social media use (Facebook, Instagram, and Twitter) on the academic performance of college students.”
• Measurable: The measurable attribute provides a clear criterion to determine if the objective has been met (Locke & Latham, 2013). A quantifiable element, such as a percentage or a number, adds a measurable quality. For example, “to increase response rate to the annual customer survey by 10%,” makes it easier to ascertain achievement.
• Achievable: The achievable aspect encourages researchers to craft realistic objectives, resembling a self-check mechanism to ensure the objectives align with the scope and resources at disposal (Doran, 1981). For example, “to interview 25 participants selected randomly from a population of 100” is an attainable objective as long as the researcher has access to these participants.
• Relevance : Relevance, the fourth element, compels the researcher to tailor the objectives in alignment with overarching goals of the study (Locke & Latham, 2013). This is extremely important – each objective must help you meet your overall one-sentence ‘aim’ in your study.
• Time-Bound: Lastly, the time-bound element fosters a sense of urgency and prioritization, preventing procrastination and enhancing productivity (Doran, 1981). “To analyze the effect of laptop use in lectures on student engagement over the course of two semesters this year” expresses a clear deadline, thus serving as a motivator for timely completion.

You’re not expected to fit every single element of the SMART framework in one objective, but across your objectives, try to touch on each of the five components.

Research Objectives Examples

1. Field: Psychology

Aim: To explore the impact of sleep deprivation on cognitive performance in college students.

• Objective 1: To compare cognitive test scores of students with less than six hours of sleep and those with 8 or more hours of sleep.
• Objective 2: To investigate the relationship between class grades and reported sleep duration.
• Objective 3: To survey student perceptions and experiences on how sleep deprivation affects their cognitive capabilities.

2. Field: Environmental Science

Aim: To understand the effects of urban green spaces on human well-being in a metropolitan city.

• Objective 1: To assess the physical and mental health benefits of regular exposure to urban green spaces.
• Objective 2: To evaluate the social impacts of urban green spaces on community interactions.
• Objective 3: To examine patterns of use for different types of urban green spaces. 

3. Field: Technology

Aim: To investigate the influence of using social media on productivity in the workplace.

• Objective 1: To measure the amount of time spent on social media during work hours.
• Objective 2: To evaluate the perceived impact of social media use on task completion and work efficiency.
• Objective 3: To explore whether company policies on social media usage correlate with different patterns of productivity.

4. Field: Education

Aim: To examine the effectiveness of online vs traditional face-to-face learning on student engagement and achievement.

• Objective 1: To compare student grades between the groups exposed to online and traditional face-to-face learning.
• Objective 2: To assess student engagement levels in both learning environments.
• Objective 3: To collate student perceptions and preferences regarding both learning methods.

5. Field: Health

Aim: To determine the impact of a Mediterranean diet on cardiac health among adults over 50.

• Objective 1: To assess changes in cardiovascular health metrics after following a Mediterranean diet for six months.
• Objective 2: To compare these health metrics with a similar group who follow their regular diet.
• Objective 3: To document participants’ experiences and adherence to the Mediterranean diet.

6. Field: Environmental Science

Aim: To analyze the impact of urban farming on community sustainability.

• Objective 1: To document the types and quantity of food produced through urban farming initiatives.
• Objective 2: To assess the effect of urban farming on local communities’ access to fresh produce.
• Objective 3: To examine the social dynamics and cooperative relationships in the creating and maintaining of urban farms.

7. Field: Sociology

Aim: To investigate the influence of home offices on work-life balance during remote work.

• Objective 1: To survey remote workers on their perceptions of work-life balance since setting up home offices.
• Objective 2: To conduct an observational study of daily work routines and family interactions in a home office setting.
• Objective 3: To assess the correlation, if any, between physical boundaries of workspaces and mental boundaries for work in the home setting.

8. Field: Economics

Aim: To evaluate the effects of minimum wage increases on small businesses.

• Objective 1: To analyze cost structures, pricing changes, and profitability of small businesses before and after minimum wage increases.
• Objective 2: To survey small business owners on the strategies they employ to navigate minimum wage increases.
• Objective 3: To examine employment trends in small businesses in response to wage increase legislation.

9. Field: Education

Aim: To explore the role of extracurricular activities in promoting soft skills among high school students.

• Objective 1: To assess the variety of soft skills developed through different types of extracurricular activities.
• Objective 2: To compare self-reported soft skills between students who participate in extracurricular activities and those who do not.
• Objective 3: To investigate the teachers’ perspectives on the contribution of extracurricular activities to students’ skill development.

10. Field: Technology

Aim: To assess the impact of virtual reality (VR) technology on the tourism industry.

• Objective 1: To document the types and popularity of VR experiences available in the tourism market.
• Objective 2: To survey tourists on their interest levels and satisfaction rates with VR tourism experiences.
• Objective 3: To determine whether VR tourism experiences correlate with increased interest in real-life travel to the simulated destinations.

11. Field: Biochemistry

Aim: To examine the role of antioxidants in preventing cellular damage.

• Objective 1: To identify the types and quantities of antioxidants in common fruits and vegetables.
• Objective 2: To determine the effects of various antioxidants on free radical neutralization in controlled lab tests.
• Objective 3: To investigate potential beneficial impacts of antioxidant-rich diets on long-term cellular health.

12. Field: Linguistics

Aim: To determine the influence of early exposure to multiple languages on cognitive development in children.

• Objective 1: To assess cognitive development milestones in monolingual and multilingual children.
• Objective 2: To document the number and intensity of language exposures for each group in the study.
• Objective 3: To investigate the specific cognitive advantages, if any, enjoyed by multilingual children.

13. Field: Art History

Aim: To explore the impact of the Renaissance period on modern-day art trends.

• Objective 1: To identify key characteristics and styles of Renaissance art.
• Objective 2: To analyze modern art pieces for the influence of the Renaissance style.
• Objective 3: To survey modern-day artists for their inspirations and the influence of historical art movements on their work.

14. Field: Cybersecurity

Aim: To assess the effectiveness of two-factor authentication (2FA) in preventing unauthorized system access.

• Objective 1: To measure the frequency of unauthorized access attempts before and after the introduction of 2FA.
• Objective 2: To survey users about their experiences and challenges with 2FA implementation.
• Objective 3: To evaluate the efficacy of different types of 2FA (SMS-based, authenticator apps, biometrics, etc.).

15. Field: Cultural Studies

Aim: To analyze the role of music in cultural identity formation among ethnic minorities.

• Objective 1: To document the types and frequency of traditional music practices within selected ethnic minority communities.
• Objective 2: To survey community members on the role of music in their personal and communal identity.
• Objective 3: To explore the resilience and transmission of traditional music practices in contemporary society.

16. Field: Astronomy

Aim: To explore the impact of solar activity on satellite communication.

• Objective 1: To categorize different types of solar activities and their frequencies of occurrence.
• Objective 2: To ascertain how variations in solar activity may influence satellite communication.
• Objective 3: To investigate preventative and damage-control measures currently in place during periods of high solar activity.

17. Field: Literature

Aim: To examine narrative techniques in contemporary graphic novels.

• Objective 1: To identify a range of narrative techniques employed in this genre.
• Objective 2: To analyze the ways in which these narrative techniques engage readers and affect story interpretation.
• Objective 3: To compare narrative techniques in graphic novels to those found in traditional printed novels.

18. Field: Renewable Energy

Aim: To investigate the feasibility of solar energy as a primary renewable resource within urban areas.

• Objective 1: To quantify the average sunlight hours across urban areas in different climatic zones. 
• Objective 2: To calculate the potential solar energy that could be harnessed within these areas.
• Objective 3: To identify barriers or challenges to widespread solar energy implementation in urban settings and potential solutions.

19. Field: Sports Science

Aim: To evaluate the role of pre-game rituals in athlete performance.

• Objective 1: To identify the variety and frequency of pre-game rituals among professional athletes in several sports.
• Objective 2: To measure the impact of pre-game rituals on individual athletes’ performance metrics.
• Objective 3: To examine the psychological mechanisms that might explain the effects (if any) of pre-game ritual on performance.

20. Field: Ecology

Aim: To investigate the effects of urban noise pollution on bird populations.

• Objective 1: To record and quantify urban noise levels in various bird habitats.
• Objective 2: To measure bird population densities in relation to noise levels.
• Objective 3: To determine any changes in bird behavior or vocalization linked to noise levels.

21. Field: Food Science

Aim: To examine the influence of cooking methods on the nutritional value of vegetables.

• Objective 1: To identify the nutrient content of various vegetables both raw and after different cooking processes.
• Objective 2: To compare the effect of various cooking methods on the nutrient retention of these vegetables.
• Objective 3: To propose cooking strategies that optimize nutrient retention.

The Importance of Research Objectives

The importance of research objectives cannot be overstated. In essence, these guideposts articulate what the researcher aims to discover, understand, or examine (Kothari, 2014).

When drafting research objectives, it’s essential to make them simple and comprehensible, specific to the point of being quantifiable where possible, achievable in a practical sense, relevant to the chosen research question, and time-constrained to ensure efficient progress (Kumar, 2019). 

Remember that a good research objective is integral to the success of your project, offering a clear path forward for setting out a research design , and serving as the bedrock of your study plan. Each objective must distinctly address a different dimension of your research question or problem (Kothari, 2014). Always bear in mind that the ultimate purpose of your research objectives is to succinctly encapsulate your aims in the clearest way possible, facilitating a coherent, comprehensive and rational approach to your planned study, and furnishing a scientific roadmap for your journey into the depths of knowledge and research (Kumar, 2019). 

Kothari, C.R (2014). Research Methodology: Methods and Techniques . New Delhi: New Age International.

Kumar, R. (2019). Research Methodology: A Step-by-Step Guide for Beginners .New York: SAGE Publications.

Doran, G. T. (1981). There’s a S.M.A.R.T. way to write management’s goals and objectives. Management review, 70 (11), 35-36.

Locke, E. A., & Latham, G. P. (2013). New Developments in Goal Setting and Task Performance . New York: Routledge.

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What Are Research Objectives and How to Write Them (with Examples)

Table of Contents

Introduction

Research is at the center of everything researchers do, and setting clear, well-defined research objectives plays a pivotal role in guiding scholars toward their desired outcomes. Research papers are essential instruments for researchers to effectively communicate their work. Among the many sections that constitute a research paper, the introduction plays a key role in providing a background and setting the context. 1 Research objectives, which define the aims of the study, are usually stated in the introduction. Every study has a research question that the authors are trying to answer, and the objective is an active statement about how the study will answer this research question. These objectives help guide the development and design of the study and steer the research in the appropriate direction; if this is not clearly defined, a project can fail!

Research studies have a research question, research hypothesis, and one or more research objectives. A research question is what a study aims to answer, and a research hypothesis is a predictive statement about the relationship between two or more variables, which the study sets out to prove or disprove. Objectives are specific, measurable goals that the study aims to achieve. The difference between these three is illustrated by the following example:

• Research question : How does low-intensity pulsed ultrasound (LIPUS) compare with a placebo device in managing the symptoms of skeletally mature patients with patellar tendinopathy?
• Research hypothesis : Pain levels are reduced in patients who receive daily active-LIPUS (treatment) for 12 weeks compared with individuals who receive inactive-LIPUS (placebo).
• Research objective : To investigate the clinical efficacy of LIPUS in the management of patellar tendinopathy symptoms.

This article discusses the importance of clear, well-thought out objectives and suggests methods to write them clearly.

What is the introduction in research papers?

Research objectives are usually included in the introduction section. This section is the first that the readers will read so it is essential that it conveys the subject matter appropriately and is well written to create a good first impression. A good introduction sets the tone of the paper and clearly outlines the contents so that the readers get a quick snapshot of what to expect.

A good introduction should aim to: 2,3

• Indicate the main subject area, its importance, and cite previous literature on the subject
• Define the gap(s) in existing research, ask a research question, and state the objectives
• Announce the present research and outline its novelty and significance
• Avoid repeating the Abstract, providing unnecessary information, and claiming novelty without accurate supporting information.

Why are research objectives important?

Objectives can help you stay focused and steer your research in the required direction. They help define and limit the scope of your research, which is important to efficiently manage your resources and time. The objectives help to create and maintain the overall structure, and specify two main things—the variables and the methods of quantifying the variables.

A good research objective:

• defines the scope of the study
• gives direction to the research
• helps maintain focus and avoid diversions from the topic
• minimizes wastage of resources like time, money, and energy

Types of research objectives

Research objectives can be broadly classified into general and specific objectives . 4 General objectives state what the research expects to achieve overall while specific objectives break this down into smaller, logically connected parts, each of which addresses various parts of the research problem. General objectives are the main goals of the study and are usually fewer in number while specific objectives are more in number because they address several aspects of the research problem.

Example (general objective): To investigate the factors influencing the financial performance of firms listed in the New York Stock Exchange market.

Example (specific objective): To assess the influence of firm size on the financial performance of firms listed in the New York Stock Exchange market.

In addition to this broad classification, research objectives can be grouped into several categories depending on the research problem, as given in Table 1.

Table 1: Types of research objectives

Characteristics of research objectives

Research objectives must start with the word “To” because this helps readers identify the objective in the absence of headings and appropriate sectioning in research papers. 5,6

• A good objective is SMART (mostly applicable to specific objectives):
• Specific—clear about the what, why, when, and how
• Measurable—identifies the main variables of the study and quantifies the targets
• Achievable—attainable using the available time and resources
• Realistic—accurately addresses the scope of the problem
• Time-bound—identifies the time in which each step will be completed
• Research objectives clarify the purpose of research.
• They help understand the relationship and dissimilarities between variables.
• They provide a direction that helps the research to reach a definite conclusion.

How to write research objectives?

Research objectives can be written using the following steps: 7

• State your main research question clearly and concisely.
• Describe the ultimate goal of your study, which is similar to the research question but states the intended outcomes more definitively.
• Divide this main goal into subcategories to develop your objectives.
• Limit the number of objectives (1-2 general; 3-4 specific)
• Assess each objective using the SMART
• Start each objective with an action verb like assess, compare, determine, evaluate, etc., which makes the research appear more actionable.
• Use specific language without making the sentence data heavy.
• The most common section to add the objectives is the introduction and after the problem statement.
• Add the objectives to the abstract (if there is one).
• State the general objective first, followed by the specific objectives.

Formulating research objectives

Formulating research objectives has the following five steps, which could help researchers develop a clear objective: 8

• Identify the research problem.
• Review past studies on subjects similar to your problem statement, that is, studies that use similar methods, variables, etc.
• Identify the research gaps the current study should cover based on your literature review. These gaps could be theoretical, methodological, or conceptual.
• Define the research question(s) based on the gaps identified.
• Revise/relate the research problem based on the defined research question and the gaps identified. This is to confirm that there is an actual need for a study on the subject based on the gaps in literature.
• Identify and write the general and specific objectives.
• Incorporate the objectives into the study.

Advantages of research objectives

Adding clear research objectives has the following advantages: 4,8

• Maintains the focus and direction of the research
• Optimizes allocation of resources with minimal wastage
• Acts as a foundation for defining appropriate research questions and hypotheses
• Provides measurable outcomes that can help evaluate the success of the research
• Determines the feasibility of the research by helping to assess the availability of required resources
• Ensures relevance of the study to the subject and its contribution to existing literature

Disadvantages of research objectives

Research objectives also have few disadvantages, as listed below: 8

• Absence of clearly defined objectives can lead to ambiguity in the research process
• Unintentional bias could affect the validity and accuracy of the research findings

Key takeaways

• Research objectives are concise statements that describe what the research is aiming to achieve.
• They define the scope and direction of the research and maintain focus.
• The objectives should be SMART—specific, measurable, achievable, realistic, and time-bound.
• Clear research objectives help avoid collection of data or resources not required for the study.
• Well-formulated specific objectives help develop the overall research methodology, including data collection, analysis, interpretation, and utilization.
• Research objectives should cover all aspects of the problem statement in a coherent way.
• They should be clearly stated using action verbs.

Frequently asked questions on research objectives

Q: what’s the difference between research objectives and aims 9.

A: Research aims are statements that reflect the broad goal(s) of the study and outline the general direction of the research. They are not specific but clearly define the focus of the study.

Example: This research aims to explore employee experiences of digital transformation in retail HR.

Research objectives focus on the action to be taken to achieve the aims. They make the aims more practical and should be specific and actionable.

Example: To observe the retail HR employees throughout the digital transformation.

Q: What are the examples of research objectives, both general and specific?

A: Here are a few examples of research objectives:

• To identify the antiviral chemical constituents in Mumbukura gitoniensis (general)
• To carry out solvent extraction of dried flowers of Mumbukura gitoniensis and isolate the constituents. (specific)
• To determine the antiviral activity of each of the isolated compounds. (specific)
• To examine the extent, range, and method of coral reef rehabilitation projects in five shallow reef areas adjacent to popular tourist destinations in the Philippines.
• To investigate species richness of mammal communities in five protected areas over the past 20 years.
• To evaluate the potential application of AI techniques for estimating best-corrected visual acuity from fundus photographs with and without ancillary information.
• To investigate whether sport influences psychological parameters in the personality of asthmatic children.

Q: How do I develop research objectives?

A: Developing research objectives begins with defining the problem statement clearly, as illustrated by Figure 1. Objectives specify how the research question will be answered and they determine what is to be measured to test the hypothesis.

Q: Are research objectives measurable?

A: The word “measurable” implies that something is quantifiable. In terms of research objectives, this means that the source and method of collecting data are identified and that all these aspects are feasible for the research. Some metrics can be created to measure your progress toward achieving your objectives.

Q: Can research objectives change during the study?

A: Revising research objectives during the study is acceptable in situations when the selected methodology is not progressing toward achieving the objective, or if there are challenges pertaining to resources, etc. One thing to keep in mind is the time and resources you would have to complete your research after revising the objectives. Thus, as long as your problem statement and hypotheses are unchanged, minor revisions to the research objectives are acceptable.

Q: What is the difference between research questions and research objectives? 10

Q: Are research objectives the same as hypotheses?

A: No, hypotheses are predictive theories that are expressed in general terms. Research objectives, which are more specific, are developed from hypotheses and aim to test them. A hypothesis can be tested using several methods and each method will have different objectives because the methodology to be used could be different. A hypothesis is developed based on observation and reasoning; it is a calculated prediction about why a particular phenomenon is occurring. To test this prediction, different research objectives are formulated. Here’s a simple example of both a research hypothesis and research objective.

Research hypothesis : Employees who arrive at work earlier are more productive.

Research objective : To assess whether employees who arrive at work earlier are more productive.

To summarize, research objectives are an important part of research studies and should be written clearly to effectively communicate your research. We hope this article has given you a brief insight into the importance of using clearly defined research objectives and how to formulate them.

• Farrugia P, Petrisor BA, Farrokhyar F, Bhandari M. Practical tips for surgical research: Research questions, hypotheses and objectives. Can J Surg. 2010 Aug;53(4):278-81.
• Abbadia J. How to write an introduction for a research paper. Mind the Graph website. Accessed June 14, 2023. https://mindthegraph.com/blog/how-to-write-an-introduction-for-a-research-paper/
• Writing a scientific paper: Introduction. UCI libraries website. Accessed June 15, 2023. https://guides.lib.uci.edu/c.php?g=334338&p=2249903
• Research objectives—Types, examples and writing guide. Researchmethod.net website. Accessed June 17, 2023. https://researchmethod.net/research-objectives/#:~:text=They%20provide%20a%20clear%20direction,track%20and%20achieve%20their%20goals .
• Bartle P. SMART Characteristics of good objectives. Community empowerment collective website. Accessed June 16, 2023. https://cec.vcn.bc.ca/cmp/modules/pd-smar.htm
• Research objectives. Studyprobe website. Accessed June 18, 2023. https://www.studyprobe.in/2022/08/research-objectives.html
• Corredor F. How to write objectives in a research paper. wikiHow website. Accessed June 18, 2023. https://www.wikihow.com/Write-Objectives-in-a-Research-Proposal
• Research objectives: Definition, types, characteristics, advantages. AccountingNest website. Accessed June 15, 2023. https://www.accountingnest.com/articles/research/research-objectives
• Phair D., Shaeffer A. Research aims, objectives & questions. GradCoach website. Accessed June 20, 2023. https://gradcoach.com/research-aims-objectives-questions/
• Understanding the difference between research questions and objectives. Accessed June 21, 2023. https://board.researchersjob.com/blog/research-questions-and-objectives

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Handy Tips To Write A Clear Research Objectives With Examples

Introduction.

Research objectives play a crucial role in any research study. They provide a clear direction and purpose for the research, guiding the researcher in their investigation. Understanding research objectives is essential for conducting a successful study and achieving meaningful results.

In this comprehensive review, we will delve into the definition of research objectives, exploring their characteristics, types, and examples. We will also discuss the relationship between research objectives and research questions, as well as provide insights into how to write effective research objectives. Additionally, we will examine the role of research objectives in research methodology and highlight the importance of them in a study. By the end of this review, you will have a comprehensive understanding of research objectives and their significance in the research process.

Definition of Research Objectives: What Are They?

A research objective is defined as a clear and concise statement that outlines the specific goals and aims of a research study. These objectives are designed to be specific, measurable, achievable, relevant, and time-bound (SMART), ensuring they provide a structured pathway to accomplishing the intended outcomes of the project. Each objective serves as a foundational element that summarizes the purpose of your study, guiding the research activities and helping to measure progress toward the study’s goals. Additionally, research objectives are integral components of the research framework , establishing a clear direction that aligns with the overall research questions and hypotheses. This alignment helps to ensure that the study remains focused and relevant, facilitating the systematic collection, analysis, and interpretation of data.

Characteristics of Effective Research Objectives

Characteristics of research objectives include:

• Specific: Research objectives should be clear about the what, why, when, and how of the study.
• Measurable: Research objectives should identify the main variables of the study that can be measured or observed.
• Relevant: Research objectives should be relevant to the research topic and contribute to the overall understanding of the subject.
• Feasible: Research objectives should be achievable within the constraints of time, resources, and expertise available.
• Logical: Research objectives should follow a logical sequence and build upon each other to achieve the overall research goal.
• Observable: Research objectives should be observable or measurable in order to assess the progress and success of the research project.
• Unambiguous: Research objectives should be clear and unambiguous, leaving no room for interpretation or confusion.
• Measurable: Research objectives should be measurable, allowing for the collection of data and analysis of results.

By incorporating these characteristics into research objectives, researchers can ensure that their study is focused, achievable, and contributes to the body of knowledge in their field.

Types of Research Objectives

Research objective can be broadly classified into general and specific objectives. General objectives are broad statements that define the overall purpose of the research. They provide a broad direction for the study and help in setting the context. Specific objectives, on the other hand, are detailed objectives that describe what will be researched during the study. They are more focused and provide specific outcomes that the researcher aims to achieve. Specific objectives are derived from the general objectives and help in breaking down the research into smaller, manageable parts. The specific objectives should be clear, measurable, and achievable. They should be designed in a way that allows the researcher to answer the research questions and address the research problem.

In addition to general and specific objectives, research objective can also be categorized as descriptive or analytical objectives. Descriptive objectives focus on describing the characteristics or phenomena of a particular subject or population. They involve surveys, observations, and data collection to provide a detailed understanding of the subject. Analytical objectives, on the other hand, aim to analyze the relationships between variables or factors. They involve data analysis and interpretation to gain insights and draw conclusions.

Both descriptive and analytical objectives are important in research as they serve different purposes and contribute to a comprehensive understanding of the research topic.

Examples of Research Objectives

Here are some examples of research objectives in different fields:

1. Objective: To identify key characteristics and styles of Renaissance art.

This objective focuses on exploring the characteristics and styles of art during the Renaissance period. The research may involve analyzing various artworks, studying historical documents, and interviewing experts in the field.

2. Objective: To analyze modern art trends and their impact on society.

This objective aims to examine the current trends in modern art and understand how they influence society. The research may involve analyzing artworks, conducting surveys or interviews with artists and art enthusiasts, and studying the social and cultural implications of modern art.

3. Objective: To investigate the effects of exercise on mental health.

This objective focuses on studying the relationship between exercise and mental health. The research may involve conducting experiments or surveys to assess the impact of exercise on factors such as stress, anxiety, and depression.

4. Objective: To explore the factors influencing consumer purchasing decisions in the fashion industry.

This objective aims to understand the various factors that influence consumers’ purchasing decisions in the fashion industry. The research may involve conducting surveys, analyzing consumer behavior data, and studying the impact of marketing strategies on consumer choices.

5. Objective: To examine the effectiveness of a new drug in treating a specific medical condition.

This objective focuses on evaluating the effectiveness of a newly developed drug in treating a particular medical condition. The research may involve conducting clinical trials, analyzing patient data, and comparing the outcomes of the new drug with existing treatment options.

These examples demonstrate the diversity of research objectives across different disciplines. Each objective is specific, measurable, and achievable, providing a clear direction for the research study.

Aligning Research Objectives with Research Questions

Research objectives and research questions are essential components of a research project. Research objective describe what you intend your research project to accomplish. They summarize the approach and purpose of the project and provide a clear direction for the research. Research questions, on the other hand, are the starting point of any good research. They guide the overall direction of the research and help identify and focus on the research gaps .

The main difference between research questions and objectives is their form. Research questions are stated in a question form, while objectives are specific, measurable, and achievable goals that you aim to accomplish within a specified timeframe. Research questions are broad statements that provide a roadmap for the research, while objectives break down the research aim into smaller, actionable steps.

Research objectives and research questions work together to form the ‘golden thread’ of a research project. The research aim specifies what the study will answer, while the objectives and questions specify how the study will answer it. They provide a clear focus and scope for the research project, helping researchers stay on track and ensure that their study is meaningful and relevant.

When writing research objectives and questions, it is important to be clear, concise, and specific. Each objective or question should address a specific aspect of the research and contribute to the overall goal of the study. They should also be measurable, meaning that their achievement can be assessed and evaluated. Additionally, research objectives and questions should be achievable within the given timeframe and resources of the research project. By clearly defining the objectives and questions, researchers can effectively plan and execute their research, leading to valuable insights and contributions to the field.

Guidelines for Writing Clear Research Objectives

Writing research objective is a crucial step in any research project. The objectives provide a clear direction and purpose for the study, guiding the researcher in their data collection and analysis. Here are some tips on how to write effective research objective:

1. Be clear and specific

Research objective should be written in a clear and specific manner. Avoid vague or ambiguous language that can lead to confusion. Clearly state what you intend to achieve through your research.

2. Use action verbs

Start your research objective with action verbs that describe the desired outcome. Action verbs such as ‘investigate’, ‘analyze’, ‘compare’, ‘evaluate’, or ‘identify’ help to convey the purpose of the study.

3. Align with research questions or hypotheses

Ensure that your research objectives are aligned with your research questions or hypotheses. The objectives should address the main goals of your study and provide a framework for answering your research questions or testing your hypotheses.

4. Be realistic and achievable

Set research objectives that are realistic and achievable within the scope of your study. Consider the available resources, time constraints, and feasibility of your objectives. Unrealistic objectives can lead to frustration and hinder the progress of your research.

5. Consider the significance and relevance

Reflect on the significance and relevance of your research objectives. How will achieving these objectives contribute to the existing knowledge or address a gap in the literature? Ensure that your objectives have a clear purpose and value.

6. Seek feedback

It is beneficial to seek feedback on your research objectives from colleagues, mentors, or experts in your field. They can provide valuable insights and suggestions for improving the clarity and effectiveness of your objectives.

7. Revise and refine

Research objectives are not set in stone. As you progress in your research, you may need to revise and refine your objectives to align with new findings or changes in the research context. Regularly review and update your objectives to ensure they remain relevant and focused.

By following these tips, you can write research objectives that are clear, focused, and aligned with your research goals. Well-defined objectives will guide your research process and help you achieve meaningful outcomes.

The Role of Research Objectives in Research Methodology

Research objectives play a crucial role in the research methodology . In research methodology, research objectives are formulated based on the research questions or problem statement. These objectives help in defining the scope and focus of the study, ensuring that the research is conducted in a systematic and organized manner.

The research objectives in research methodology act as a roadmap for the research project. They help in identifying the key variables to be studied, determining the research design and methodology, and selecting the appropriate data collection methods .

Furthermore, research objectives in research methodology assist in evaluating the success of the study. By setting clear objectives, researchers can assess whether the desired outcomes have been achieved and determine the effectiveness of the research methods employed. It is important to note that research objectives in research methodology should be aligned with the overall research aim. They should address the specific aspects or components of the research aim and provide a framework for achieving the desired outcomes.

Understanding The Dynamic of Research Objectives in Your Study

The research objectives of a study play a crucial role in guiding the research process, ensuring that the study is focused, purposeful, and contributes to the advancement of knowledge in the field. It is important to note that the research objectives may evolve or change as the study progresses. As new information is gathered and analyzed, the researcher may need to revise the objectives to ensure that they remain relevant and achievable.

In summary, research objectives are essential components in writing an effective research paper . They provide a roadmap for the research process, guiding the researcher in their investigation and helping to ensure that the study is purposeful and meaningful. By understanding and effectively utilizing research objectives, researchers can enhance the quality and impact of their research endeavors.

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Research Objectives | Examples & How To Write Them

Introduction

Why are research objectives important, characteristics of research objectives, what is an example of a research objective, types of research objectives, formulating research objectives.

Research objectives are clear, concise statements that outline what a research project aims to achieve. They guide the direction of the study, ensuring that researchers stay focused and organized. Properly formulated objectives help in identifying the scope of the research and the methods to be used. This article will cover the importance of research objectives, their characteristics, examples, types, and how to write them. By understanding these elements, researchers can develop effective research aims that enhance the clarity and purpose of their studies. This straightforward approach will provide practical guidance for both novice and experienced researchers in crafting clear research objectives.

Research objectives are crucial because they provide a clear focus and direction for a study. A well-defined research aim can help researchers stay on track by outlining specific goals that need to be achieved. This clarity ensures that all aspects of the research are aligned with the intended outcomes.

Having well-defined objectives also facilitates effective planning and execution. Researchers can allocate resources more efficiently, select appropriate methodologies , and set realistic timelines. Moreover, clear objectives help in the assessment and evaluation of the research process and its outcomes, making it easier to determine whether the study has been successful.

Research objectives also enhance communication. They allow researchers to clearly convey the purpose and scope of their study to stakeholders, including funding bodies, academic peers, and participants. This transparency builds credibility and trust, which are essential for the integrity of the research process.

Research objectives possess several key characteristics that make them effective and useful for guiding a study. These characteristics ensure that the objectives are clear, achievable, and relevant to the research problem . Here are the essential characteristics to keep in mind when you develop research objectives for a successful research project.

• Specific : Research objectives focus on what the researcher intends to accomplish in a precise and unambiguous manner. Specific objectives break a research study into discrete and manageable tasks.
• Measurable : Objectives should include criteria for measuring progress and success. This characteristic allows researchers to track their progress and determine whether the objectives have been achieved.
• Achievable : Objectives need to be realistic and attainable within the constraints of the research project, including time, resources, and expertise. Setting achievable goals prevents frustration and ensures steady progress.
• Relevant : Objectives must be aligned with the research problem and the overall purpose of the study. They should contribute directly to addressing the study's research question or research hypothesis.
• Time-bound : Objectives should have a clear timeline for completion. This characteristic helps in planning and managing the research process, ensuring that objectives are met within a specified period.
• Clear and concise : Objectives should be articulated in a straightforward manner, avoiding complex language and jargon. Clarity helps in communicating the objectives to others involved in the research process.
• Focused : Each objective should target a specific aspect of the research problem. Having focused objectives prevents the study from becoming too broad and unmanageable.
• Logical : Objectives should follow a logical sequence that reflects the research process. This characteristic ensures that the objectives build on one another and collectively contribute to the overall research aim.
• Feasible : Consider the availability of resources, including data, equipment, and personnel, when formulating objectives. Feasibility ensures that the objectives can be realistically achieved with the available resources.
• Ethical : Objectives should respect ethical standards and guidelines . They should consider the well-being of participants, confidentiality , and integrity of the research process.

By adhering to these characteristics, researchers can develop objectives that are effective in guiding their study. Clear and well-defined objectives not only enhance the research process but also improve the quality and credibility of the research outcomes.

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To illustrate what a well-defined research objective might look like, consider a study focused on improving reading comprehension among elementary school students. The research aims to evaluate the effectiveness of a new instructional strategy designed to enhance students' reading skills. Here is an example of a research objective for this study:

"To assess the impact of the interactive reading program on the reading comprehension scores of third-grade students over a six-month period."

This objective is effective because it meets several key criteria:

• Specific : The objective clearly states what will be assessed (the impact of the interactive reading program) and the target group (third-grade students).
• Measurable : The impact will be measured by changes in reading comprehension scores, providing a clear metric for evaluation.
• Achievable : The objective is realistic and attainable within a six-month period, assuming the necessary resources and support are available.
• Relevant : The objective is directly related to the study's research questions , which is to improve reading comprehension among elementary school students.
• Time-bound : The objective specifies a six-month period for the assessment, ensuring that the study is conducted within a defined timeframe.

By formulating objectives in this manner, researchers can create a clear roadmap for their study from research design to research paper . This example demonstrates how to incorporate the essential characteristics of research objectives into a practical and actionable statement.

Well-defined objectives help in planning the study, selecting an appropriate research methodology , and evaluating the outcomes. They also facilitate effective communication among members of the research team and with stakeholders, ensuring that everyone involved understands the purpose and scope of the research.

Research objectives can be categorized into different types based on their purpose and focus. Understanding these types helps researchers design studies that effectively address their research questions . Here are three common types of research objectives:

Descriptive objectives

Descriptive objectives aim to describe the characteristics or functions of a particular phenomenon or group. These objectives are often used in exploratory studies to gather information and provide a detailed picture of the subject being investigated. For example, a descriptive objective might be, "To describe the dietary habits of teenagers in urban areas." This type of objective helps in understanding the current state or conditions of the research subject.

Exploratory objectives

Exploratory objectives seek to explore new areas of knowledge or investigate relationships between variables. These objectives are often used in the initial stages of research to identify patterns, generate propositions, or uncover insights that can lead to further studies. An example of an exploratory objective is, "To investigate the relationship between social media usage and academic performance among college students." This type of objective is useful for studies that aim to look into new or under-researched areas.

Explanatory objectives

Explanatory objectives aim to explain the causes or reasons behind a particular phenomenon. These objectives often involve verifying a theory or determining relationships among variables. For instance, an explanatory objective could be, "To determine the impact of a structured exercise program on the mental health of elderly individuals." This type of objective is essential for studies that seek to understand the underlying mechanisms or effects of specific interventions or conditions.

Writing research aims is a critical step in the research process . Well-defined objectives provide a roadmap for the study and help ensure that the research stays focused and relevant. Here are some steps to guide the formulation of research objectives:

• Identify the research problem : Start by clearly defining the research problem or question you aim to address. Understanding the core issue helps in developing objectives that are directly related to the research focus.
• Conduct a literature review : Review existing research related to your topic to identify gaps in knowledge and areas that need further investigation. This background information can help in shaping specific and relevant objectives.
• Define the scope : Determine the scope of your study by considering factors such as the population, setting, and time frame. This will help in setting realistic and achievable objectives.
• Use the SMART criteria : Ensure that your objectives are Specific, Measurable, Achievable, Relevant, and Time-bound. This framework helps in creating clear and focused objectives that can guide the research process effectively.
• Break down the main objective : If your research has a broad aim, break it down into smaller, more specific sub-objectives. This makes the research more manageable and allows for a systematic approach to addressing the main research problem.
• Phrase objectives clearly : Write your objectives in clear and concise language, avoiding jargon and complex terms. Each objective should be easy to understand and communicate to others involved in the research.
• Align with research questions : Ensure that each objective aligns with your research question(s). The objectives should directly contribute to answering the key questions posed by your study.
• Seek feedback : Discuss your research objectives with peers, advisors, or experts in the field. Feedback can help refine the objectives and ensure that they are realistic and relevant.

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• Defining Research Objectives: How To  Write Them

Almost all industries use research for growth and development. Research objectives are how researchers ensure that their study has direction and makes a significant contribution to growing an industry or niche.

Research objectives provide a clear and concise statement of what the researcher wants to find out. As a researcher, you need to clearly outline and define research objectives to guide the research process and ensure that the study is relevant and generates the impact you want.

In this article, we will explore research objectives and how to leverage them to achieve successful research studies.

What Are Research Objectives?

Research objectives are what you want to achieve through your research study. They guide your research process and help you focus on the most important aspects of your topic.

You can also define the scope of your study and set realistic and attainable study goals with research objectives. For example, with clear research objectives, your study focuses on the specific goals you want to achieve and prevents you from spending time and resources collecting unnecessary data.

However, sticking to research objectives isn’t always easy, especially in broad or unconventional research. This is why most researchers follow the SMART criteria when defining their research objectives.

Understanding SMART Criteria in Research

Think of research objectives as a roadmap to achieving your research goals, with the SMART criteria as your navigator on the map.

SMART stands for Specific, Measurable, Achievable, Relevant, and Time-bound. These criteria help you ensure that your research objectives are clear, specific, realistic, meaningful, and time-bound.

Here’s a breakdown of the SMART Criteria:

Specific : Your research objectives should be clear: what do you want to achieve, why do you want to achieve it, and how do you plan to achieve it? Avoid vague or broad statements that don’t provide enough direction for your research.

Measurable : Your research objectives should have metrics that help you track your progress and measure your results. Also, ensure the metrics are measurable with data to verify them.

Achievable : Your research objectives should be within your research scope, timeframe, and budget. Also, set goals that are challenging but not impossible.

Relevant: Your research objectives should be in line with the goal and significance of your study. Also, ensure that the objectives address a specific issue or knowledge gap that is interesting and relevant to your industry or niche.

Time-bound : Your research objectives should have a specific deadline or timeframe for completion. This will help you carefully set a schedule for your research activities and milestones and monitor your study progress.

Characteristics of Effective Research Objectives

Clarity : Your objectives should be clear and unambiguous so that anyone who reads them can understand what you intend to do. Avoid vague or general terms that could be taken out of context.

Specificity : Your objectives should be specific and address the research questions that you have formulated. Do not use broad or narrow objectives as they may restrict your field of research or make your research irrelevant.

Measurability : Define your metrics with indicators or metrics that help you determine if you’ve accomplished your goals or not. This will ensure you are tracking the research progress and making interventions when needed.

Also, do use objectives that are subjective or based on personal opinions, as they may be difficult to accurately verify and measure.

Achievability : Your objectives should be realistic and attainable, given the resources and time available for your research project. You should set objectives that match your skills and capabilities, they can be difficult but not so hard that they are realistically unachievable.

For example, setting very difficult make you lose confidence, and abandon your research. Also, setting very simple objectives could demotivate you and prevent you from closing the knowledge gap or making significant contributions to your field with your research.

Relevance : Your objectives should be relevant to your research topic and contribute to the existing knowledge in your field. Avoid objectives that are unrelated or insignificant, as they may waste your time or resources.

Time-bound : Your objectives should be time-bound and specify when you will complete them. Have a realistic and flexible timeframe for achieving your objectives, and track your progress with it. 

Steps to Writing Research Objectives

Identify the research questions.

The first step in writing effective research objectives is to identify the research questions that you are trying to answer. Research questions help you narrow down your topic and identify the gaps or problems that you want to address with your research.

For example, if you are interested in the impact of technology on children’s development, your research questions could be:

• What is the relationship between technology use and academic performance among children?
• Are children who use technology more likely to do better in school than those who do not?
• What is the social and psychological impact of technology use on children?

Brainstorm Objectives

Once you have your research questions, you can brainstorm possible objectives that relate to them. Objectives are more specific than research questions, and they tell you what you want to achieve or learn in your research.

You can use verbs such as analyze, compare, evaluate, explore, investigate, etc. to express your objectives. Also, try to generate as many objectives as possible, without worrying about their quality or feasibility at this stage.

Prioritize Objectives

Once you’ve brainstormed your objectives, you’ll need to prioritize them based on their relevance and feasibility. Relevance is how relevant the objective is to your research topic and how well it fits into your overall research objective.

Feasibility is how realistic and feasible the objective is compared to the time, money, and expertise you have. You can create a matrix or ranking system to organize your objectives and pick the ones that matter the most.

Refine Objectives

The next step is to refine and revise your objectives to ensure clarity and specificity. Start by ensuring that your objectives are consistent and coherent with each other and with your research questions. 

Make Objectives SMART

A useful way to refine your objectives is to make them SMART, which stands for specific, measurable, achievable, relevant, and time-bound. 

• Specific : Objectives should clearly state what you hope to achieve.
• Measurable : They should be able to be quantified or evaluated.
• Achievable : realistic and within the scope of the research study.
• Relevant : They should be directly related to the research questions.
• Time-bound : specific timeframe for research completion.

Review and Finalize Objectives

The final step is to review your objectives for coherence and alignment with your research questions and aim. Ensure your objectives are logically connected and consistent with each other and with the purpose of your study.

You also need to check that your objectives are not too broad or too narrow, too easy or too hard, too many or too few. You can use a checklist or a rubric to evaluate your objectives and make modifications.

Examples of Well-Written Research Objectives

Example 1- Psychology

Research question: What are the effects of social media use on teenagers’ mental health?

Objective : To determine the relationship between the amount of time teenagers in the US spend on social media and their levels of anxiety and depression before and after using social media.

What Makes the Research Objective SMART?

The research objective is specific because it clearly states what the researcher hopes to achieve. It is measurable because it can be quantified by measuring the levels of anxiety and depression in teenagers. 

Also, the objective is achievable because the researcher can collect enough data to answer the research question. It is relevant because it is directly related to the research question. It is time-bound because it has a specific deadline for completion.

Example 2- Marketing

Research question : How can a company increase its brand awareness by 10%?

Objective : To develop a marketing strategy that will increase the company’s sales by 10% within the next quarter.

How Is this Research Objective SMART?

The research states what the researcher hopes to achieve ( Specific ). You can also measure the company’s reach before and after the marketing plan is implemented ( Measurable ).

The research objective is also achievable because you can develop a marketing plan that will increase awareness by 10% within the timeframe. The objective is directly related to the research question ( Relevant ). It is also time-bound because it has a specific deadline for completion.

Research objectives are a well-designed roadmap to completing and achieving your overall research goal. 

However, research goals are only effective if they are well-defined and backed up with the best practices such as the SMART criteria. Properly defining research objectives will help you plan and conduct your research project effectively and efficiently.

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• research goals
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• smart goals
• SMART research objectives
• Moradeke Owa

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Writing the Research Objectives: 5 Straightforward Examples

The research objective of a research proposal or scientific article defines the direction or content of a research investigation. Without the research objectives, the proposal or research paper is in disarray. It is like a fisherman riding on a boat without any purpose and with no destination in sight. Therefore, at the beginning of any research venture, the researcher must be clear about what he or she intends to do or achieve in conducting a study.

How do you define the objectives of a study? What are the uses of the research objective? How would a researcher write this essential part of the research? This article aims to provide answers to these questions.

Table of Contents

Definition of a research objective.

A research objective describes, in a few words, the result of the research project after its implementation. It answers the question,

The research objective provides direction to the performance of the study.

What are the Uses of the Research Objective?

The uses of the research objective are enumerated below:

The research design serves as the “blueprint” for the research investigation. The University of Southern California describes the different types of research design extensively. It details the data to be gathered, data collection procedure, data measurement, and statistical tests to use in the analysis.

The variables of the study include those factors that the researcher wants to evaluate in the study. These variables narrow down the research to several manageable components to see differences or correlations between them.

Specifying the data collection procedure ensures data accuracy and integrity . Thus, the probability of error is minimized. Generalizations or conclusions based on valid arguments founded on reliable data strengthens research findings on particular issues and problems.

How is the Research Objective Written?

A research objective must be achievable, i.e., it must be framed keeping in mind the available time, infrastructure required for research, and other resources.

Before forming a research objective, you should read about all the developments in your area of research and find gaps in knowledge that need to be addressed. Readings will help you come up with suitable objectives for your research project.

5 Examples of Research Objectives

The following examples of research objectives based on several published studies on various topics demonstrate how the research objectives are written:

Finally, writing the research objectives requires constant practice, experience, and knowledge about the topic investigated. Clearly written objectives save time, money, and effort.

Evans, K. L., Rodrigues, A. S., Chown, S. L., & Gaston, K. J. (2006). Protected areas and regional avian species richness in South Africa.  Biology letters ,  2 (2), 184-188.

Yeemin, T., Sutthacheep, M., & Pettongma, R. (2006). Coral reef restoration projects in Thailand.  Ocean & Coastal Management ,  49 (9-10), 562-575.

© 2020 March 23 P. A. Regoniel Updated 17 November 2020 | Updated 18 January 2024

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What is a Research Objective? Definition, Types, Examples and Best Practices

By Nick Jain

Published on: September 8, 2023

Table of Contents

What is a Research Objective?

Types of research objectives, top 6 examples of research objectives, research objectives best practices.

A research objective is defined as a clear and concise statement of the specific goals and aims of a research study. It outlines what the researcher intends to accomplish and what they hope to learn or discover through their research. Research objectives are crucial for guiding the research process and ensuring that the study stays focused and on track.

Key characteristics of research objectives include:

• Clarity: Research objectives should be clearly defined and easy to understand. One should ensure there is no space for ambiguity or misinterpretation.
• Specificity: Objectives should be specific and narrowly focused on the aspects of the research topic that the study intends to investigate. They should answer the question of “what” or “which” rather than “how” or “why.”
• Measurability: Research objectives should be formulated in a way that allows for measurement and evaluation. This means that there should be a way to determine whether the objectives have been achieved or not.
• Relevance: Objectives should be relevant to the research topic and align with the overall research question or hypothesis. They should address important aspects of the subject matter.
• Realistic: Objectives should be attainable within the constraints of the study, including time, resources, and feasibility.
• Time-bound: Research objectives may have associated timelines or deadlines to indicate when the research aims should be accomplished.

Research objectives help researchers stay focused on the purpose of their study and guide the development of research methods , data collection, and analysis. They also serve as a basis for evaluating the success of the research once it’s completed. In the context of a research project, research objectives typically follow the formulation of a research question or hypothesis and serve as a roadmap for conducting the study.

Research objectives can be categorized into different types based on their focus and purpose within a research study. Here are some common types of research objectives:

1. Descriptive Objectives

These objectives aim to provide a detailed and accurate description of a phenomenon, event, or subject. They focus on answering questions about what, who, where, and when.

Example: “To delineate the demographic attributes of the study’s participants.”

2. Exploratory Objectives

Exploratory objectives are used when researchers seek to gain a better understanding of a topic, especially when there is limited existing knowledge. They often involve preliminary investigations.

Example: “To investigate the possible determinants impacting consumer inclinations towards sustainable products.”

3. Explanatory Objectives

Explanatory objectives are designed to identify the relationships between variables and explain the causes or reasons behind certain phenomena.

Example: “To examine the causal relationship between smoking habits and the development of lung cancer.”

4. Comparative Objectives

These objectives involve comparing two or more variables, groups, or situations to identify similarities, differences, patterns, or trends.

Example: “To compare the performance of two different marketing strategies in terms of their impact on sales.”

5. Predictive Objectives

Predictive objectives aim to forecast or predict future outcomes or trends based on existing data or patterns.

Example: “To forecast customer attrition rates within an online subscription service by utilizing historical usage patterns and satisfaction data.”

6. Normative Objectives

Normative objectives involve establishing standards, guidelines, or recommendations for a specific area of study.

Example: “To develop industry-specific ethical guidelines for the responsible use of artificial intelligence.”

7. Qualitative Objectives

Qualitative objectives are used in qualitative research to explore and understand experiences, perceptions, and behaviors in-depth.

Example: “To reveal the latent motivations and emotions of participants within a qualitative interview investigation.”

8. Quantitative Objectives

Quantitative objectives involve the collection and analysis of numerical data to measure and quantify specific phenomena.

Example: “To ascertain the relationship between income levels and the availability of educational resources among a selected group of households.”

9. Longitudinal Objectives

Longitudinal objectives involve studying the same subjects or entities over an extended period to track changes or developments.

Example: “To assess the cognitive development of children from kindergarten through high school graduation.”

10. Cross-Sectional Objectives

Cross-sectional objectives involve the study of a sample at a single point in time to gather data about a population’s characteristics or attitudes.

Example: “To assess the present employment situation and job satisfaction levels among healthcare sector employees.”

The choice of research objective type depends on the nature of the research , the research questions or hypotheses, and the overall goals of the study. Researchers often use a combination of these types to address different aspects of their research inquiries.

Learn more: What is Research Design?

Research objectives can vary widely depending on the field of study and the specific research topic. However, I can provide you with some examples of research objectives in different domains to illustrate their diversity:

1. Healthcare Research

• To investigate the relationship between regular physical activity and the incidence of cardiovascular diseases in adults aged 40-60.
• To assess the effectiveness of a new drug in reducing symptoms of a specific medical condition over a six-month period.
• To identify the factors influencing healthcare-seeking behavior among a specific demographic group.

2. Educational Research

• To examine the impact of technology integration in the classroom on students’ academic performance in mathematics.
• To determine the effectiveness of a new teaching method for improving reading comprehension in elementary school children.
• To explore the factors that contribute to student dropout rates in a particular educational institution.

3. Environmental Science Research

• To analyze the effects of climate change on the migration patterns of a specific bird species in a particular region.
• To investigate the long-term impact of deforestation on local biodiversity in a tropical rainforest.
• To assess the effectiveness of a conservation program in preserving a critically endangered species.

4. Business and Marketing Research

• To evaluate consumer preferences for eco-friendly packaging materials in the cosmetics industry.
• To analyze the market potential for a new product in a specific geographical area.
• To identify the key factors influencing customer loyalty in the fast-food restaurant industry.

5. Social Science Research

• To examine the relationship between socioeconomic status and access to quality healthcare in urban areas.
• To investigate the influence of adolescents’ use of social media on their mental well-being.
• To investigate the factors contributing to workplace diversity and inclusion in a multinational corporation.

6. Psychological Research

• To investigate the effects of mindfulness meditation on reducing symptoms of anxiety in adults.
• To explore the relationship between early childhood experiences and attachment styles in adulthood.
• To analyze the factors influencing decision-making in individuals with specific personality traits.

These examples cover a range of research objectives across different disciplines. Keep in mind that research objectives should be tailored to the specific research question or hypothesis and should be formulated to guide the research process effectively.

Learn more: What is Voice of Customer Research?

Creating effective research objectives is essential for conducting a successful research study. Here are some best practices to keep in mind when formulating research objectives:

• Be Specific and Clear: Research objectives should be precise and unambiguous. They should clearly state what the study aims to achieve, leaving no room for misinterpretation.
• Align with Research Questions or Hypotheses: Ensure that your research objectives directly align with the broader research questions or hypotheses that guide your study. They should help you address those overarching inquiries.
• Use Action Verbs: Start your research objectives with action verbs that describe what you intend to do. Common action verbs include “to investigate,” “to analyze,” “to examine,” “to compare,” “to determine,” etc.
• Focus on Measurable Outcomes: Make sure your research objectives are formulated in a way that allows for measurement and evaluation. There should be a way to determine whether the objectives have been achieved or not.
• Be Realistic and Feasible: Set research objectives that are attainable within the constraints of your study, including available time, budget, and resources. Unrealistic objectives can lead to frustration and failure.
• Consider the Scope of the Study: Keep the scope of your research in mind when defining objectives. Ensure that your objectives are neither too broad nor too narrow. They should be manageable within the context of your study.
• Prioritize Objectives: If you have multiple research objectives, consider prioritizing them. Identify which objectives are most crucial to the success of your study and allocate resources accordingly.
• Ensure Relevance: Objectives should be directly relevant to the research topic and the overall purpose of the study. Avoid including objectives that do not contribute to answering your research questions or testing your hypotheses.
• Consider the Target Audience: Think about who will be reading your research objectives. They should be understandable to both experts in your field and non-expert stakeholders.
• Review and Refine: It’s a good practice to review and refine your research objectives after initial formulation. Seek feedback from peers, advisors, or mentors to ensure they are well-constructed and aligned with your study’s goals.
• Specific: Clarify the specific goal or objective you would like to achieve.
• Measurable: Include criteria for measuring success.
• Achievable: Set realistic and attainable objectives.
• Relevant: Ensure they are relevant to your research .
• Time-bound: Include a timeframe for achieving each objective.
• Be Open to Adaptation: Research objectives may evolve as your study progresses and new information emerges. Be open to adapting them if necessary to better align with your research findings and goals.
• Document Your Objectives: Keep a clear record of your research objectives in your research proposal, plan, or protocol. This documentation helps maintain focus throughout the study.

By following these best practices, you can create research objectives that guide your study effectively and contribute to its success in achieving its intended outcomes.

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• Aims and Objectives – A Guide for Academic Writing
• Doing a PhD

One of the most important aspects of a thesis, dissertation or research paper is the correct formulation of the aims and objectives. This is because your aims and objectives will establish the scope, depth and direction that your research will ultimately take. An effective set of aims and objectives will give your research focus and your reader clarity, with your aims indicating what is to be achieved, and your objectives indicating how it will be achieved.

Introduction

There is no getting away from the importance of the aims and objectives in determining the success of your research project. Unfortunately, however, it is an aspect that many students struggle with, and ultimately end up doing poorly. Given their importance, if you suspect that there is even the smallest possibility that you belong to this group of students, we strongly recommend you read this page in full.

This page describes what research aims and objectives are, how they differ from each other, how to write them correctly, and the common mistakes students make and how to avoid them. An example of a good aim and objectives from a past thesis has also been deconstructed to help your understanding.

What Are Aims and Objectives?

Research aims.

A research aim describes the main goal or the overarching purpose of your research project.

In doing so, it acts as a focal point for your research and provides your readers with clarity as to what your study is all about. Because of this, research aims are almost always located within its own subsection under the introduction section of a research document, regardless of whether it’s a thesis , a dissertation, or a research paper .

A research aim is usually formulated as a broad statement of the main goal of the research and can range in length from a single sentence to a short paragraph. Although the exact format may vary according to preference, they should all describe why your research is needed (i.e. the context), what it sets out to accomplish (the actual aim) and, briefly, how it intends to accomplish it (overview of your objectives).

To give an example, we have extracted the following research aim from a real PhD thesis:

Example of a Research Aim

The role of diametrical cup deformation as a factor to unsatisfactory implant performance has not been widely reported. The aim of this thesis was to gain an understanding of the diametrical deformation behaviour of acetabular cups and shells following impaction into the reamed acetabulum. The influence of a range of factors on deformation was investigated to ascertain if cup and shell deformation may be high enough to potentially contribute to early failure and high wear rates in metal-on-metal implants.

Note: Extracted with permission from thesis titled “T he Impact And Deformation Of Press-Fit Metal Acetabular Components ” produced by Dr H Hothi of previously Queen Mary University of London.

Research Objectives

Where a research aim specifies what your study will answer, research objectives specify how your study will answer it.

They divide your research aim into several smaller parts, each of which represents a key section of your research project. As a result, almost all research objectives take the form of a numbered list, with each item usually receiving its own chapter in a dissertation or thesis.

Following the example of the research aim shared above, here are it’s real research objectives as an example:

Example of a Research Objective

• Develop finite element models using explicit dynamics to mimic mallet blows during cup/shell insertion, initially using simplified experimentally validated foam models to represent the acetabulum.
• Investigate the number, velocity and position of impacts needed to insert a cup.
• Determine the relationship between the size of interference between the cup and cavity and deformation for different cup types.
• Investigate the influence of non-uniform cup support and varying the orientation of the component in the cavity on deformation.
• Examine the influence of errors during reaming of the acetabulum which introduce ovality to the cavity.
• Determine the relationship between changes in the geometry of the component and deformation for different cup designs.
• Develop three dimensional pelvis models with non-uniform bone material properties from a range of patients with varying bone quality.
• Use the key parameters that influence deformation, as identified in the foam models to determine the range of deformations that may occur clinically using the anatomic models and if these deformations are clinically significant.

It’s worth noting that researchers sometimes use research questions instead of research objectives, or in other cases both. From a high-level perspective, research questions and research objectives make the same statements, but just in different formats.

Taking the first three research objectives as an example, they can be restructured into research questions as follows:

Restructuring Research Objectives as Research Questions

• Can finite element models using simplified experimentally validated foam models to represent the acetabulum together with explicit dynamics be used to mimic mallet blows during cup/shell insertion?
• What is the number, velocity and position of impacts needed to insert a cup?
• What is the relationship between the size of interference between the cup and cavity and deformation for different cup types?

Difference Between Aims and Objectives

Hopefully the above explanations make clear the differences between aims and objectives, but to clarify:

• The research aim focus on what the research project is intended to achieve; research objectives focus on how the aim will be achieved.
• Research aims are relatively broad; research objectives are specific.
• Research aims focus on a project’s long-term outcomes; research objectives focus on its immediate, short-term outcomes.
• A research aim can be written in a single sentence or short paragraph; research objectives should be written as a numbered list.

How to Write Aims and Objectives

Before we discuss how to write a clear set of research aims and objectives, we should make it clear that there is no single way they must be written. Each researcher will approach their aims and objectives slightly differently, and often your supervisor will influence the formulation of yours on the basis of their own preferences.

Regardless, there are some basic principles that you should observe for good practice; these principles are described below.

Your aim should be made up of three parts that answer the below questions:

• Why is this research required?
• What is this research about?
• How are you going to do it?

The easiest way to achieve this would be to address each question in its own sentence, although it does not matter whether you combine them or write multiple sentences for each, the key is to address each one.

The first question, why , provides context to your research project, the second question, what , describes the aim of your research, and the last question, how , acts as an introduction to your objectives which will immediately follow.

Scroll through the image set below to see the ‘why, what and how’ associated with our research aim example.

Note: Your research aims need not be limited to one. Some individuals per to define one broad ‘overarching aim’ of a project and then adopt two or three specific research aims for their thesis or dissertation. Remember, however, that in order for your assessors to consider your research project complete, you will need to prove you have fulfilled all of the aims you set out to achieve. Therefore, while having more than one research aim is not necessarily disadvantageous, consider whether a single overarching one will do.

Research Objectives

Each of your research objectives should be SMART :

• Specific – is there any ambiguity in the action you are going to undertake, or is it focused and well-defined?
• Measurable – how will you measure progress and determine when you have achieved the action?
• Achievable – do you have the support, resources and facilities required to carry out the action?
• Relevant – is the action essential to the achievement of your research aim?
• Timebound – can you realistically complete the action in the available time alongside your other research tasks?

In addition to being SMART, your research objectives should start with a verb that helps communicate your intent. Common research verbs include:

Table of Research Verbs to Use in Aims and Objectives

Last, format your objectives into a numbered list. This is because when you write your thesis or dissertation, you will at times need to make reference to a specific research objective; structuring your research objectives in a numbered list will provide a clear way of doing this.

To bring all this together, let’s compare the first research objective in the previous example with the above guidance:

Checking Research Objective Example Against Recommended Approach

Research Objective:

1. Develop finite element models using explicit dynamics to mimic mallet blows during cup/shell insertion, initially using simplified experimentally validated foam models to represent the acetabulum.

Checking Against Recommended Approach:

Q: Is it specific? A: Yes, it is clear what the student intends to do (produce a finite element model), why they intend to do it (mimic cup/shell blows) and their parameters have been well-defined ( using simplified experimentally validated foam models to represent the acetabulum ).

Q: Is it measurable? A: Yes, it is clear that the research objective will be achieved once the finite element model is complete.

Q: Is it achievable? A: Yes, provided the student has access to a computer lab, modelling software and laboratory data.

Q: Is it relevant? A: Yes, mimicking impacts to a cup/shell is fundamental to the overall aim of understanding how they deform when impacted upon.

Q: Is it timebound? A: Yes, it is possible to create a limited-scope finite element model in a relatively short time, especially if you already have experience in modelling.

Q: Does it start with a verb? A: Yes, it starts with ‘develop’, which makes the intent of the objective immediately clear.

Q: Is it a numbered list? A: Yes, it is the first research objective in a list of eight.

Mistakes in Writing Research Aims and Objectives

1. making your research aim too broad.

Having a research aim too broad becomes very difficult to achieve. Normally, this occurs when a student develops their research aim before they have a good understanding of what they want to research. Remember that at the end of your project and during your viva defence , you will have to prove that you have achieved your research aims; if they are too broad, this will be an almost impossible task. In the early stages of your research project, your priority should be to narrow your study to a specific area. A good way to do this is to take the time to study existing literature, question their current approaches, findings and limitations, and consider whether there are any recurring gaps that could be investigated .

Note: Achieving a set of aims does not necessarily mean proving or disproving a theory or hypothesis, even if your research aim was to, but having done enough work to provide a useful and original insight into the principles that underlie your research aim.

2. Making Your Research Objectives Too Ambitious

Be realistic about what you can achieve in the time you have available. It is natural to want to set ambitious research objectives that require sophisticated data collection and analysis, but only completing this with six months before the end of your PhD registration period is not a worthwhile trade-off.

3. Formulating Repetitive Research Objectives

Each research objective should have its own purpose and distinct measurable outcome. To this effect, a common mistake is to form research objectives which have large amounts of overlap. This makes it difficult to determine when an objective is truly complete, and also presents challenges in estimating the duration of objectives when creating your project timeline. It also makes it difficult to structure your thesis into unique chapters, making it more challenging for you to write and for your audience to read.

Fortunately, this oversight can be easily avoided by using SMART objectives.

Hopefully, you now have a good idea of how to create an effective set of aims and objectives for your research project, whether it be a thesis, dissertation or research paper. While it may be tempting to dive directly into your research, spending time on getting your aims and objectives right will give your research clear direction. This won’t only reduce the likelihood of problems arising later down the line, but will also lead to a more thorough and coherent research project.

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• Writing Tips

How to Write Research Objectives

• 3-minute read
• 22nd November 2021

Writing a research paper, thesis, or dissertation ? If so, you’ll want to state your research objectives in the introduction of your paper to make it clear to your readers what you’re trying to accomplish. But how do you write effective research objectives? In this post, we’ll look at two key topics to help you do this:

• How to use your research aims as a basis for developing objectives.
• How to use SMART criteria to refine your research objectives.

For more advice on how to write strong research objectives, see below.

Research Aims and Objectives

There is an important difference between research aims and research objectives:

• A research aim defines the main purpose of your research. As such, you can think of your research aim as answering the question “What are you doing?”
• Research objectives (as most studies will have more than one) are the steps you will take to fulfil your aims. As such, your objectives should answer the question “How are you conducting your research?”

For instance, an example research aim could be:

This study will investigate the link between dehydration and the incidence of urinary tract infections (UTIs) in intensive care patients in Australia.

To develop a set of research objectives, you would then break down the various steps involved in meeting said aim. For example:

This study will investigate the link between dehydration and the incidence of urinary tract infections (UTIs) in intensive care patients in Australia. To achieve this, the study objectives w ill include:

• Replicat ing a small Singaporean study into the role of dehydration in UTIs in hospital patients (Sepe, 2018) in a larger Australian cohort.
• Trialing the use of intravenous fluids for intensive care patients to prevent dehydration.
• Assessing the relationship between the age of patients and quantities of intravenous fluids needed to counter dehydration.

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Note that the objectives don’t go into any great detail here. The key is to briefly summarize each component of your study. You can save details for how you will conduct the research for the methodology section of your paper.

Make Your Research Objectives SMART

A great way to refine your research objectives is to use SMART criteria . Borrowed from the world of project management, there are many versions of this system. However, we’re going to focus on developing specific, measurable, achievable, relevant, and timebound objectives.

In other words, a good research objective should be all of the following:

• S pecific – Is the objective clear and well-defined?
• M easurable – How will you know when the objective has been achieved? Is there a way to measure the thing you’re seeking to do?
• A chievable – Do you have the support and resources necessary to undertake this action? Are you being overly ambitious with this objective?
• R elevant – Is this objective vital for fulfilling your research aim?
• T imebound – Can this action be realistically undertaken in the time you have?

If you follow this system, your research objectives will be much stronger.

Expert Research Proofreading

Whatever your research aims and objectives, make sure to have your academic writing proofread by the experts!

Our academic editors can help you with research papers and proposals , as well as any other scholarly document you need checking. And this will help to ensure that your academic writing is always clear, concise, and precise.

Submit a free sample document today to trial our services and find out more.

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Formulating Research Aims and Objectives

Formulating research aim and objectives in an appropriate manner is one of the most important aspects of your thesis. This is because research aim and objectives determine the scope, depth and the overall direction of the research. Research question is the central question of the study that has to be answered on the basis of research findings.

Research aim emphasizes what needs to be achieved within the scope of the research, by the end of the research process. Achievement of research aim provides answer to the research question.

Research objectives divide research aim into several parts and address each part separately. Research aim specifies WHAT needs to be studied and research objectives comprise a number of steps that address HOW research aim will be achieved.

As a rule of dumb, there would be one research aim and several research objectives. Achievement of each research objective will lead to the achievement of the research aim.

Consider the following as an example:

Research title: Effects of organizational culture on business profitability: a case study of Virgin Atlantic

Research aim: To assess the effects of Virgin Atlantic organizational culture on business profitability

Following research objectives would facilitate the achievement of this aim:

• Analyzing the nature of organizational culture at Virgin Atlantic by September 1, 2022
• Identifying factors impacting Virgin Atlantic organizational culture by September 16, 2022
• Analyzing impacts of Virgin Atlantic organizational culture on employee performances by September 30, 2022
• Providing recommendations to Virgin Atlantic strategic level management in terms of increasing the level of effectiveness of organizational culture by October 5, 2022

Figure below illustrates additional examples in formulating research aims and objectives:

Formulation of research question, aim and objectives

Common mistakes in the formulation of research aim relate to the following:

1. Choosing the topic too broadly . This is the most common mistake. For example, a research title of “an analysis of leadership practices” can be classified as too broad because the title fails to answer the following questions:

a) Which aspects of leadership practices? Leadership has many aspects such as employee motivation, ethical behaviour, strategic planning, change management etc. An attempt to cover all of these aspects of organizational leadership within a single research will result in an unfocused and poor work.

b) An analysis of leadership practices in which country? Leadership practices tend to be different in various countries due to cross-cultural differences, legislations and a range of other region-specific factors. Therefore, a study of leadership practices needs to be country-specific.

c) Analysis of leadership practices in which company or industry? Similar to the point above, analysis of leadership practices needs to take into account industry-specific and/or company-specific differences, and there is no way to conduct a leadership research that relates to all industries and organizations in an equal manner.

Accordingly, as an example “a study into the impacts of ethical behaviour of a leader on the level of employee motivation in US healthcare sector” would be a more appropriate title than simply “An analysis of leadership practices”.

2. Setting an unrealistic aim . Formulation of a research aim that involves in-depth interviews with Apple strategic level management by an undergraduate level student can be specified as a bit over-ambitious. This is because securing an interview with Apple CEO Tim Cook or members of Apple Board of Directors might not be easy. This is an extreme example of course, but you got the idea. Instead, you may aim to interview the manager of your local Apple store and adopt a more feasible strategy to get your dissertation completed.

3. Choosing research methods incompatible with the timeframe available . Conducting interviews with 20 sample group members and collecting primary data through 2 focus groups when only three months left until submission of your dissertation can be very difficult, if not impossible. Accordingly, timeframe available need to be taken into account when formulating research aims and objectives and selecting research methods.

Moreover, research objectives need to be formulated according to SMART principle,

 where the abbreviation stands for specific, measurable, achievable, realistic, and time-bound.

Examples of SMART research objectives

At the conclusion part of your research project you will need to reflect on the level of achievement of research aims and objectives. In case your research aims and objectives are not fully achieved by the end of the study, you will need to discuss the reasons. These may include initial inappropriate formulation of research aims and objectives, effects of other variables that were not considered at the beginning of the research or changes in some circumstances during the research process.

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Research Topics – Ideas and Examples

Table of Contents

Research Topic

Definition:

Research topic is a specific subject or area of interest that a researcher wants to investigate or explore in-depth through research. It is the overarching theme or question that guides a research project and helps to focus the research activities towards a clear objective.

How to Choose Research Topic

You can Choose a Research Topic by following the below guide:

Identify your Interests

One of the most important factors to consider when choosing a research topic is your personal interest. This is because you will be spending a considerable amount of time researching and writing about the topic, so it’s essential that you are genuinely interested and passionate about it. Start by brainstorming a list of potential research topics based on your interests, hobbies, or areas of expertise. You can also consider the courses that you’ve enjoyed the most or the topics that have stood out to you in your readings.

Review the Literature

Before deciding on a research topic, you need to understand what has already been written about it. Conducting a preliminary review of the existing literature in your field can help you identify gaps in knowledge, inconsistencies in findings, or unanswered questions that you can explore further. You can do this by reading academic articles, books, and other relevant sources in your field. Make notes of the themes or topics that emerge and use this information to guide your research question.

Consult with your Advisor

Your academic advisor or a mentor in your field can provide you with valuable insights and guidance on choosing a research topic. They can help you identify areas of interest, suggest potential research questions, and provide feedback on the feasibility of your research proposal. They can also direct you towards relevant literature and resources that can help you develop your research further.

Consider the Scope and Feasibility

The research topic you choose should be manageable within the time and resource constraints of your project. Be mindful of the scope of your research and ensure that you are not trying to tackle a topic that is too broad or too narrow. If your topic is too broad, you may find it challenging to conduct a comprehensive analysis, while if it’s too narrow, you may struggle to find enough material to support your research.

Brainstorm with Peers

Discussing potential research topics with your peers or colleagues can help you generate new ideas and perspectives. They may have insights or expertise that you haven’t considered, and their feedback can help you refine your research question. You can also join academic groups or attend conferences in your field to network with other researchers and get inspiration for your research.

Consider the Relevance

Choose a research topic that is relevant to your field of study and has the potential to contribute to the existing knowledge. You can consider the latest trends and emerging issues in your field to identify topics that are both relevant and interesting. Conducting research on a topic that is timely and relevant can also increase the likelihood of getting published or presenting your research at conferences.

Keep an Open Mind

While it’s essential to choose a research topic that aligns with your interests and expertise, you should also be open to exploring new ideas or topics that may be outside of your comfort zone. Consider researching a topic that challenges your assumptions or introduces new perspectives that you haven’t considered before. You may discover new insights or perspectives that can enrich your research and contribute to your growth as a researcher.

Components of Research Topic

A research topic typically consists of several components that help to define and clarify the subject matter of the research project. These components include:

• Research problem or question: This is the central issue or inquiry that the research seeks to address. It should be well-defined and focused, with clear boundaries that limit the scope of the research.
• Background and context: This component provides the necessary background information and context for the research topic. It explains why the research problem or question is important, relevant, and timely. It may also include a literature review that summarizes the existing research on the topic.
• Objectives or goals : This component outlines the specific objectives or goals that the research seeks to achieve. It should be clear and concise, and should align with the research problem or question.
• Methodology : This component describes the research methods and techniques that will be used to collect and analyze data. It should be detailed enough to provide a clear understanding of how the research will be conducted, including the sampling method, data collection tools, and statistical analyses.
• Significance or contribution : This component explains the significance or contribution of the research topic. It should demonstrate how the research will add to the existing knowledge in the field, and how it will benefit practitioners, policymakers, or society at large.
• Limitations: This component outlines the limitations of the research, including any potential biases, assumptions, or constraints. It should be transparent and honest about the potential shortcomings of the research, and how these limitations will be addressed.
• Expected outcomes or findings : This component provides an overview of the expected outcomes or findings of the research project. It should be realistic and based on the research objectives and methodology.

Purpose of Research Topic

The purpose of a research topic is to identify a specific area of inquiry that the researcher wants to explore and investigate. A research topic is typically a broad area of interest that requires further exploration and refinement through the research process. It provides a clear focus and direction for the research project, and helps to define the research questions and objectives. A well-defined research topic also helps to ensure that the research is relevant and useful, and can contribute to the existing body of knowledge in the field. Ultimately, the purpose of a research topic is to generate new insights, knowledge, and understanding about a particular phenomenon, issue, or problem.

Characteristics of Research Topic

some common characteristics of a well-defined research topic include:

• Relevance : A research topic should be relevant and significant to the field of study and address a current issue, problem, or gap in knowledge.
• Specificity : A research topic should be specific enough to allow for a focused investigation and clear understanding of the research question.
• Feasibility : A research topic should be feasible, meaning it should be possible to carry out the research within the given constraints of time, resources, and expertise.
• Novelty : A research topic should add to the existing body of knowledge by introducing new ideas, concepts, or theories.
• Clarity : A research topic should be clearly articulated and easy to understand, both for the researcher and for potential readers of the research.
• Importance : A research topic should be important and have practical implications for the field or society as a whole.
• Significance : A research topic should be significant and have the potential to generate new insights and understanding in the field.

Examples of Research Topics

Here are some examples of research topics that are currently relevant and in-demand in various fields:

• The impact of social media on mental health: With the rise of social media use, this topic has gained significant attention in recent years. Researchers could investigate how social media affects self-esteem, body image, and other mental health concerns.
• The use of artificial intelligence in healthcare: As healthcare becomes increasingly digitalized, researchers could explore the use of AI algorithms to predict and prevent disease, optimize treatment plans, and improve patient outcomes.
• Renewable energy and sustainable development: As the world seeks to reduce its carbon footprint, researchers could investigate the potential of renewable energy sources such as wind and solar power, and how these technologies can be integrated into existing infrastructure.
• The impact of workplace diversity and inclusion on employee productivity: With an increasing focus on diversity and inclusion in the workplace, researchers could investigate how these factors affect employee morale, productivity, and retention.
• Cybersecurity and data privacy: As data breaches and cyber attacks become more common, researchers could explore new methods of protecting sensitive information and preventing malicious attacks.
• T he impact of mindfulness and meditation on stress reduction: As stress-related health issues become more prevalent, researchers could investigate the effectiveness of mindfulness and meditation practices on reducing stress and improving overall well-being.

Research Topics Ideas

Here are some Research Topics Ideas from different fields:

• The impact of social media on mental health and well-being.
• The effectiveness of various teaching methods in improving academic performance in high schools.
• The role of AI and machine learning in healthcare: current applications and future potentials.
• The impact of climate change on wildlife habitats and conservation efforts.
• The effects of video game violence on aggressive behavior in young adults.
• The effectiveness of mindfulness-based stress reduction techniques in reducing anxiety and depression.
• The impact of technology on human relationships and social interactions.
• The role of exercise in promoting physical and mental health in older adults.
• The causes and consequences of income inequality in developed and developing countries.
• The effects of cultural diversity in the workplace on job satisfaction and productivity.
• The impact of remote work on employee productivity and work-life balance.
• The relationship between sleep patterns and cognitive functioning.
• The effectiveness of online learning versus traditional classroom learning.
• The role of government policies in promoting renewable energy adoption.
• The effects of childhood trauma on mental health in adulthood.
• The impact of social media on political participation and civic engagement.
• The effectiveness of cognitive-behavioral therapy in treating anxiety disorders.
• The relationship between nutrition and cognitive functioning.
• The impact of gentrification on urban communities.
• The effects of music on mood and emotional regulation.
• The impact of microplastics on marine ecosystems and food webs.
• The role of artificial intelligence in detecting and preventing cyberattacks.
• The effectiveness of mindfulness-based interventions in managing chronic pain.
• The relationship between personality traits and job satisfaction.
• The effects of social isolation on mental and physical health in older adults.
• The impact of cultural and linguistic diversity on healthcare access and outcomes.
• The effectiveness of psychotherapy in treating depression and anxiety in adolescents.
• The relationship between exercise and cognitive aging.
• The effects of social media on body image and self-esteem.
• The role of corporate social responsibility in promoting sustainable business practices.
• The impact of mindfulness meditation on attention and focus in children.
• The relationship between political polarization and media consumption habits.
• The effects of urbanization on mental health and well-being.
• The role of social support in managing chronic illness.
• The impact of social media on romantic relationships and dating behaviors.
• The effectiveness of behavioral interventions in promoting physical activity in sedentary adults.
• The relationship between sleep quality and immune function.
• The effects of workplace diversity and inclusion programs on employee retention.
• The impact of climate change on global food security.
• The role of music therapy in improving communication and social skills in individuals with autism spectrum disorder.
• The impact of cultural values on the development of mental health stigma.
• The effectiveness of mindfulness-based stress reduction techniques in reducing burnout in healthcare professionals.
• The relationship between social media use and body dissatisfaction among adolescents.
• The effects of nature exposure on cognitive functioning and well-being.
• The role of peer mentoring in promoting academic success in underrepresented student populations.
• The impact of neighborhood characteristics on physical activity and obesity.
• The effectiveness of cognitive rehabilitation interventions in improving cognitive functioning in individuals with traumatic brain injury.
• The relationship between organizational culture and employee job satisfaction.
• The effects of cultural immersion experiences on intercultural competence development.
• The role of assistive technology in promoting independence and quality of life for individuals with disabilities.
• The impact of workplace design on employee productivity and well-being.
• The impact of digital technologies on the music industry and artist revenues.
• The effectiveness of cognitive behavioral therapy in treating insomnia.
• The relationship between social media use and body weight perception among young adults.
• The effects of green spaces on mental health and well-being in urban areas.
• The role of mindfulness-based interventions in reducing substance use disorders.
• The impact of workplace bullying on employee turnover and job satisfaction.
• The effectiveness of animal-assisted therapy in treating mental health disorders.
• The relationship between teacher-student relationships and academic achievement.
• The effects of social support on resilience in individuals experiencing adversity.
• The role of cognitive aging in driving safety and mobility.
• The effectiveness of psychotherapy in treating post-traumatic stress disorder (PTSD).
• The relationship between social media use and sleep quality.
• The effects of cultural competency training on healthcare providers’ attitudes and behaviors towards diverse patient populations.
• The role of exercise in preventing chronic diseases such as type 2 diabetes and cardiovascular disease.
• The impact of the gig economy on job security and worker rights.
• The effectiveness of art therapy in promoting emotional regulation and coping skills in children and adolescents.
• The relationship between parenting styles and child academic achievement.
• The effects of social comparison on well-being and self-esteem.
• The role of nutrition in promoting healthy aging and longevity.
• The impact of gender diversity in leadership on organizational performance.
• The effectiveness of family-based interventions in treating eating disorders.
• The relationship between social media use and perceived loneliness among older adults.
• The effects of mindfulness-based interventions on pain management in chronic pain patients.
• The role of physical activity in preventing and treating depression.
• The impact of cultural differences on communication and conflict resolution in international business.
• The effectiveness of eye movement desensitization and reprocessing (EMDR) in treating anxiety disorders.
• The relationship between student engagement and academic success in higher education.
• The effects of discrimination on mental health outcomes in minority populations.
• The role of virtual reality in enhancing learning experiences.
• The impact of social media influencers on consumer behavior and brand loyalty.
• The effectiveness of acceptance and commitment therapy (ACT) in treating chronic pain.
• The relationship between social media use and body image dissatisfaction among men.
• The effects of exposure to nature on cognitive functioning and creativity.
• The role of spirituality in coping with illness and disability.
• The impact of automation on employment and job displacement.
• The effectiveness of dialectical behavior therapy (DBT) in treating borderline personality disorder.
• The relationship between teacher-student relationships and school attendance.
• The effects of mindfulness-based interventions on workplace stress and burnout.
• The role of exercise in promoting cognitive functioning and brain health in older adults.
• The impact of diversity and inclusion initiatives on organizational innovation and creativity.
• The effectiveness of cognitive remediation therapy in treating schizophrenia.
• The relationship between social media use and body dissatisfaction among women.
• The effects of exposure to natural light on mood and sleep quality.
• The role of spirituality in enhancing well-being and resilience in military personnel.
• The impact of artificial intelligence on job training and skill development.
• The effectiveness of interpersonal therapy (IPT) in treating depression.
• The relationship between parental involvement and academic achievement among low-income students.
• The effects of mindfulness-based interventions on emotional regulation and coping skills in trauma survivors.
• The role of nutrition in preventing and treating mental health disorders.

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Research Question Examples 🧑🏻‍🏫

25+ Practical Examples & Ideas To Help You Get Started 

By: Derek Jansen (MBA) | October 2023

A well-crafted research question (or set of questions) sets the stage for a robust study and meaningful insights.  But, if you’re new to research, it’s not always clear what exactly constitutes a good research question. In this post, we’ll provide you with clear examples of quality research questions across various disciplines, so that you can approach your research project with confidence!

Research Question Examples

• Psychology research questions
• Business research questions
• Education research questions
• Healthcare research questions
• Computer science research questions

Examples: Psychology

Let’s start by looking at some examples of research questions that you might encounter within the discipline of psychology.

How does sleep quality affect academic performance in university students?

This question is specific to a population (university students) and looks at a direct relationship between sleep and academic performance, both of which are quantifiable and measurable variables.

What factors contribute to the onset of anxiety disorders in adolescents?

The question narrows down the age group and focuses on identifying multiple contributing factors. There are various ways in which it could be approached from a methodological standpoint, including both qualitatively and quantitatively.

Do mindfulness techniques improve emotional well-being?

This is a focused research question aiming to evaluate the effectiveness of a specific intervention.

How does early childhood trauma impact adult relationships?

This research question targets a clear cause-and-effect relationship over a long timescale, making it focused but comprehensive.

Is there a correlation between screen time and depression in teenagers?

This research question focuses on an in-demand current issue and a specific demographic, allowing for a focused investigation. The key variables are clearly stated within the question and can be measured and analysed (i.e., high feasibility).

Examples: Business/Management

Next, let’s look at some examples of well-articulated research questions within the business and management realm.

How do leadership styles impact employee retention?

This is an example of a strong research question because it directly looks at the effect of one variable (leadership styles) on another (employee retention), allowing from a strongly aligned methodological approach.

What role does corporate social responsibility play in consumer choice?

Current and precise, this research question can reveal how social concerns are influencing buying behaviour by way of a qualitative exploration.

Does remote work increase or decrease productivity in tech companies?

Focused on a particular industry and a hot topic, this research question could yield timely, actionable insights that would have high practical value in the real world.

How do economic downturns affect small businesses in the homebuilding industry?

Vital for policy-making, this highly specific research question aims to uncover the challenges faced by small businesses within a certain industry.

Which employee benefits have the greatest impact on job satisfaction?

By being straightforward and specific, answering this research question could provide tangible insights to employers.

Examples: Education

Next, let’s look at some potential research questions within the education, training and development domain.

How does class size affect students’ academic performance in primary schools?

This example research question targets two clearly defined variables, which can be measured and analysed relatively easily.

Do online courses result in better retention of material than traditional courses?

Timely, specific and focused, answering this research question can help inform educational policy and personal choices about learning formats.

What impact do US public school lunches have on student health?

Targeting a specific, well-defined context, the research could lead to direct changes in public health policies.

To what degree does parental involvement improve academic outcomes in secondary education in the Midwest?

This research question focuses on a specific context (secondary education in the Midwest) and has clearly defined constructs.

What are the negative effects of standardised tests on student learning within Oklahoma primary schools?

This research question has a clear focus (negative outcomes) and is narrowed into a very specific context.

Need a helping hand?

Examples: Healthcare

Shifting to a different field, let’s look at some examples of research questions within the healthcare space.

What are the most effective treatments for chronic back pain amongst UK senior males?

Specific and solution-oriented, this research question focuses on clear variables and a well-defined context (senior males within the UK).

How do different healthcare policies affect patient satisfaction in public hospitals in South Africa?

This question is has clearly defined variables and is narrowly focused in terms of context.

Which factors contribute to obesity rates in urban areas within California?

This question is focused yet broad, aiming to reveal several contributing factors for targeted interventions.

Does telemedicine provide the same perceived quality of care as in-person visits for diabetes patients?

Ideal for a qualitative study, this research question explores a single construct (perceived quality of care) within a well-defined sample (diabetes patients).

Which lifestyle factors have the greatest affect on the risk of heart disease?

This research question aims to uncover modifiable factors, offering preventive health recommendations.

Examples: Computer Science

Last but certainly not least, let’s look at a few examples of research questions within the computer science world.

What are the perceived risks of cloud-based storage systems?

Highly relevant in our digital age, this research question would align well with a qualitative interview approach to better understand what users feel the key risks of cloud storage are.

Which factors affect the energy efficiency of data centres in Ohio?

With a clear focus, this research question lays a firm foundation for a quantitative study.

How do TikTok algorithms impact user behaviour amongst new graduates?

While this research question is more open-ended, it could form the basis for a qualitative investigation.

What are the perceived risk and benefits of open-source software software within the web design industry?

Practical and straightforward, the results could guide both developers and end-users in their choices.

Remember, these are just examples…

In this post, we’ve tried to provide a wide range of research question examples to help you get a feel for what research questions look like in practice. That said, it’s important to remember that these are just examples and don’t necessarily equate to good research topics . If you’re still trying to find a topic, check out our topic megalist for inspiration.

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This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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Research Objectives

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Research objectives are specific goals or purposes that guide a study or investigation. They are clearly defined statements that outline what the researcher aims to achieve through their research . These objectives help to focus the study, provide direction, and establish the scope of the research design . They typically include the main questions or problems the research seeks to address and are essential for designing the methodology, data collection, and analysis processes. By defining research objectives , researchers can ensure their study remains on track and addresses the key issues relevant to their topic.

What Are Research Objectives?

Research objectives are clear, specific goals that guide a study’s direction and scope. They outline what the researcher aims to achieve, helping to focus the research, design methodologies, and guide data collection and analysis. These objectives ensure the research stays on track and addresses key issues relevant to the topic.

Examples of Research Objectives

• To determine the impact of social media on adolescent mental health.
• To analyze the effectiveness of online learning platforms in primary education.
• To investigate the relationship between diet and cognitive function in adults.
• To assess customer satisfaction with a new product line.
• To explore the effects of climate change on local agriculture.
• To identify key factors influencing employee job satisfaction in the tech industry.
• To evaluate the success of a community health intervention program.
• To compare the environmental benefits of electric vs. hybrid vehicles.
• To examine the role of leadership styles in organizational performance.
• To measure the economic impact of tourism in a specific region.

Examples of Qualitative Research Objectives

• To explore the experiences of patients undergoing chronic pain management.
• To understand the perceptions of teachers on the integration of technology in the classroom.
• To investigate the motivations behind volunteerism in community service organizations.
• To examine the cultural influences on dietary habits among immigrant families.
• To assess the impact of workplace culture on employee morale in remote teams.
• To identify the challenges faced by first-generation college students in higher education.
• To analyze the role of social support networks in the lives of single parents.
• To study the decision-making processes of consumers when choosing organic products.
• To explore the lived experiences of individuals recovering from addiction.
• To understand the factors influencing career choices among high school students.

Examples of Research Objectives in a Research Proposal

• To investigate the effects of social media usage on high school students’ academic performance.
• To explore the relationship between work-life balance and job satisfaction among healthcare professionals.
• To assess the impact of urban green spaces on residents’ mental health in metropolitan areas.
• To analyze the effectiveness of bilingual education programs in enhancing language proficiency among elementary students.
• To determine the influence of corporate social responsibility initiatives on consumer loyalty in the retail industry.
• To examine the role of leadership styles in fostering innovation within technology startups.
• To identify barriers to accessing healthcare services in rural communities.
• To evaluate the success of digital marketing strategies in small businesses.
• To understand the factors affecting voter turnout in local elections.
• To study the impact of remote work on team collaboration and productivity in the IT sector.

Research Objectives in Business

• To evaluate the effectiveness of digital marketing strategies in increasing online sales.
• To analyze customer satisfaction levels with the company’s new product line.
• To investigate the impact of employee training programs on productivity.
• To determine the key factors influencing consumer purchasing decisions in the retail industry.
• To assess the role of corporate social responsibility in enhancing brand reputation.
• To explore the relationship between workplace diversity and employee performance.
• To examine the effects of remote work on team collaboration and company culture.
• To identify market trends and opportunities for business expansion in emerging markets.
• To study the influence of pricing strategies on customer retention and loyalty.
• To measure the impact of leadership styles on organizational innovation and growth.

Why are Research Objectives Important?

Research objectives are crucial because they provide clear direction and focus for a study, ensuring that the research stays on track and addresses the specific goals set by the researcher. They help in the formulation of research questions and the design of the methodology, guiding data collection and analysis processes. Well-defined objectives make it easier to measure the study’s success and ensure that the findings are relevant and meaningful. They also enhance the credibility and reliability of the research by outlining a precise plan, making it easier for others to understand and replicate the study.

Importance of Research Objectives

• Provide Clarity and Focus: Research objectives clearly outline what the study aims to achieve, helping to narrow down the scope and maintain a clear direction throughout the research process.
• Guide Research Design: They help in formulating research questions and determining the most appropriate methodology, ensuring that data collection and analysis are aligned with the study’s goals.
• Ensure Relevance: Well-defined objectives ensure that the research addresses specific, relevant issues, making the findings more meaningful and applicable.
• Measure Success: They provide benchmarks against which the success of the study can be measured, making it easier to assess whether the research has achieved its intended goals.
• Enhance Credibility: Clearly stated objectives enhance the credibility and reliability of the research by demonstrating a systematic and organized approach.

Types of Research Objectives

1. Descriptive Objectives

Descriptive objectives aim to describe the characteristics or functions of a particular phenomenon or population. These objectives focus on answering the “what” aspect of research. Example : To describe the demographic characteristics of smartphone users in the United States.

2. Exploratory Objectives

Exploratory objectives aim to explore new areas where little information is available. They seek to gain insights and familiarize the researcher with the subject area. Example : To explore the potential factors influencing consumer preferences for electric vehicles.

3. Explanatory Objectives

Explanatory objectives aim to explain the relationships or causality between variables. These objectives focus on understanding the “why” and “how” aspects of research. Example : To explain the relationship between social media usage and academic performance among college students.

4. Predictive Objectives

Predictive objectives aim to predict the future trends, behaviors, or outcomes based on current data or trends. These objectives are used to forecast and anticipate future scenarios. Example : To predict the impact of climate change on agricultural productivity over the next decade.

5. Evaluative Objectives

Evaluative objectives aim to assess the effectiveness or impact of an intervention, program, or policy. These objectives focus on determining the success or value of something. Example : To evaluate the effectiveness of a new employee training program on job performance.

Characteristics of Research Objectives

Research objectives are crucial components of any study as they define the purpose and goals of the research. Well-crafted research objectives provide clarity, direction, and focus to the study. Here are the key characteristics of research objectives:

1. Specific

Research objectives should be clear and precise, leaving no room for ambiguity. They should clearly state what the research intends to achieve. Example: Specific Objective: “To determine the impact of social media marketing on consumer purchasing decisions.”

2. Measurable

Objectives should be quantifiable, allowing researchers to assess the extent to which they have been achieved. This involves using metrics or indicators that can be measured. Example: Measurable Objective: “To measure the increase in sales by 15% after implementing a social media marketing campaign.”

3. Achievable

The objectives should be realistic and attainable within the scope and resources of the study. Setting achievable goals ensures that the research can be completed successfully. Example: Achievable Objective: “To survey 500 consumers within a three-month period to understand their social media usage patterns.”

4. Relevant

Objectives must be relevant to the research problem and aligned with the overall purpose of the study. They should address the key issues and contribute to solving the research problem. Example: Relevant Objective: “To analyze the relationship between social media engagement and brand loyalty among teenagers.”

5. Time-bound

Objectives should have a clear timeframe within which they are to be achieved. This helps in planning and maintaining the research schedule. Example: Time-bound Objective: “To complete data collection within six months and publish findings within one year.”

How to write Research Objectives?

1. identify the research problem.

• Clearly define the research problem.
• Understand the main issue or question you want to address.

2. Conduct a Literature Review

• Review existing literature to understand what has already been done in the field.
• Refine your research problem and identify gaps.

3. Define the Scope of Your Study

• Determine the boundaries of your research.
• Specify what aspects of the problem you will address and what you will exclude.

4. Formulate Specific Questions

• Break down your research problem into specific, clear, and focused questions.

5. Use Action Verbs

• Use specific action verbs such as “analyze,” “determine,” “evaluate,” “explore,” and “compare” to articulate your aims.

6. Be Clear and Concise

• Ensure your objectives are clear, concise, and easy to understand.
• Avoid ambiguous language and make each objective specific and measurable.

7. Align with Research Goals

• Ensure that your objectives are aligned with the overall goals of your research.
• Each objective should contribute to achieving these goals.

8. Prioritize Objectives

• List your objectives in order of importance.
• Focus on primary objectives first, followed by secondary ones.

9. Ensure Feasibility

• Make sure your objectives are realistic and achievable within the scope of your resources, time, and capabilities.

10. Review and Refine

• Review your objectives to ensure they are comprehensive and cover all aspects of your research problem.
• Refine them as necessary for clarity and focus.

Advantages and Disadvantages of Research Objectives

Advantages of research objectives.

• Clarity and Focus Objectives provide a clear direction, helping you stay on track. Example: “To study the impact of social media on teenagers’ mental health” keeps your research focused on a specific topic.
• Guidance for Methodology They help in choosing the right methods and tools for your research. Example: “To test the effectiveness of new teaching methods” suggests using experiments and tests.
• Measurement and Evaluation Objectives make it easy to measure success. Example: “To improve test scores by 15% with a new teaching method” provides a clear goal to aim for.
• Resource Allocation They ensure efficient use of time, money, and effort. Example: If your objective is “To survey 500 people,” you can plan your resources accordingly.
• Communication Objectives help explain your research to others. Example: Clear objectives can be shared in grant proposals to get funding.

Disadvantages of Research Objectives

• Rigidity Objectives can be too rigid, limiting flexibility. Example: If new data shows something unexpected, a fixed objective might stop you from exploring it.
• Over-Simplification They might oversimplify complex issues. Example: “To study the effect of diet on health” might ignore the many factors that influence health.
• Bias Introduction Specific objectives can lead to focusing too narrowly. Example: Studying only the positive effects of a new drug might overlook side effects.
• Pressure to Achieve They can create pressure to meet specific outcomes, risking research integrity. Example: You might feel pressured to show that a new teaching method works, even if it doesn’t.
• Resource Constraints Some objectives may require more resources than available. Example: “To survey 1,000 people” might be hard if you have limited funds.

FAQ’s

Why are research objectives important.

Research objectives provide direction and focus for the study, ensuring that the research stays on track and addresses relevant questions.

How do you formulate research objectives?

Formulate research objectives by identifying key questions your research aims to answer, ensuring they are specific, measurable, achievable, relevant, and time-bound (SMART).

What is the difference between research objectives and research questions?

Research objectives outline the goals of the study, while research questions specify what the researcher aims to find out.

Can research objectives change during the study?

Yes, research objectives can be refined or adjusted as the study progresses, especially if new insights emerge.

How many research objectives should a study have?

The number of research objectives depends on the scope of the study but typically ranges from two to five.

How do research objectives relate to hypotheses?

Research objectives guide the study, while hypotheses are testable predictions derived from these objectives.

Can research objectives be qualitative or quantitative?

Yes, research objectives can be either qualitative, focusing on understanding phenomena, or quantitative, focusing on measuring variables.

How do you prioritize research objectives?

Prioritize research objectives based on their relevance to the research problem and feasibility within the study’s constraints.

What role do research objectives play in a literature review?

Research objectives help structure the literature review, guiding the selection of relevant studies and identifying gaps in existing research.

How do research objectives influence data collection?

Research objectives determine the type of data needed and the appropriate methods for collecting this data.

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• v.53(4); 2010 Aug

Research questions, hypotheses and objectives

Patricia farrugia.

* Michael G. DeGroote School of Medicine, the

Bradley A. Petrisor

† Division of Orthopaedic Surgery and the

Forough Farrokhyar

‡ Departments of Surgery and

§ Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ont

Mohit Bhandari

There is an increasing familiarity with the principles of evidence-based medicine in the surgical community. As surgeons become more aware of the hierarchy of evidence, grades of recommendations and the principles of critical appraisal, they develop an increasing familiarity with research design. Surgeons and clinicians are looking more and more to the literature and clinical trials to guide their practice; as such, it is becoming a responsibility of the clinical research community to attempt to answer questions that are not only well thought out but also clinically relevant. The development of the research question, including a supportive hypothesis and objectives, is a necessary key step in producing clinically relevant results to be used in evidence-based practice. A well-defined and specific research question is more likely to help guide us in making decisions about study design and population and subsequently what data will be collected and analyzed. 1

Objectives of this article

In this article, we discuss important considerations in the development of a research question and hypothesis and in defining objectives for research. By the end of this article, the reader will be able to appreciate the significance of constructing a good research question and developing hypotheses and research objectives for the successful design of a research study. The following article is divided into 3 sections: research question, research hypothesis and research objectives.

Research question

Interest in a particular topic usually begins the research process, but it is the familiarity with the subject that helps define an appropriate research question for a study. 1 Questions then arise out of a perceived knowledge deficit within a subject area or field of study. 2 Indeed, Haynes suggests that it is important to know “where the boundary between current knowledge and ignorance lies.” 1 The challenge in developing an appropriate research question is in determining which clinical uncertainties could or should be studied and also rationalizing the need for their investigation.

Increasing one’s knowledge about the subject of interest can be accomplished in many ways. Appropriate methods include systematically searching the literature, in-depth interviews and focus groups with patients (and proxies) and interviews with experts in the field. In addition, awareness of current trends and technological advances can assist with the development of research questions. 2 It is imperative to understand what has been studied about a topic to date in order to further the knowledge that has been previously gathered on a topic. Indeed, some granting institutions (e.g., Canadian Institute for Health Research) encourage applicants to conduct a systematic review of the available evidence if a recent review does not already exist and preferably a pilot or feasibility study before applying for a grant for a full trial.

In-depth knowledge about a subject may generate a number of questions. It then becomes necessary to ask whether these questions can be answered through one study or if more than one study needed. 1 Additional research questions can be developed, but several basic principles should be taken into consideration. 1 All questions, primary and secondary, should be developed at the beginning and planning stages of a study. Any additional questions should never compromise the primary question because it is the primary research question that forms the basis of the hypothesis and study objectives. It must be kept in mind that within the scope of one study, the presence of a number of research questions will affect and potentially increase the complexity of both the study design and subsequent statistical analyses, not to mention the actual feasibility of answering every question. 1 A sensible strategy is to establish a single primary research question around which to focus the study plan. 3 In a study, the primary research question should be clearly stated at the end of the introduction of the grant proposal, and it usually specifies the population to be studied, the intervention to be implemented and other circumstantial factors. 4

Hulley and colleagues 2 have suggested the use of the FINER criteria in the development of a good research question ( Box 1 ). The FINER criteria highlight useful points that may increase the chances of developing a successful research project. A good research question should specify the population of interest, be of interest to the scientific community and potentially to the public, have clinical relevance and further current knowledge in the field (and of course be compliant with the standards of ethical boards and national research standards).

FINER criteria for a good research question

Adapted with permission from Wolters Kluwer Health. 2

Whereas the FINER criteria outline the important aspects of the question in general, a useful format to use in the development of a specific research question is the PICO format — consider the population (P) of interest, the intervention (I) being studied, the comparison (C) group (or to what is the intervention being compared) and the outcome of interest (O). 3 , 5 , 6 Often timing (T) is added to PICO ( Box 2 ) — that is, “Over what time frame will the study take place?” 1 The PICOT approach helps generate a question that aids in constructing the framework of the study and subsequently in protocol development by alluding to the inclusion and exclusion criteria and identifying the groups of patients to be included. Knowing the specific population of interest, intervention (and comparator) and outcome of interest may also help the researcher identify an appropriate outcome measurement tool. 7 The more defined the population of interest, and thus the more stringent the inclusion and exclusion criteria, the greater the effect on the interpretation and subsequent applicability and generalizability of the research findings. 1 , 2 A restricted study population (and exclusion criteria) may limit bias and increase the internal validity of the study; however, this approach will limit external validity of the study and, thus, the generalizability of the findings to the practical clinical setting. Conversely, a broadly defined study population and inclusion criteria may be representative of practical clinical practice but may increase bias and reduce the internal validity of the study.

PICOT criteria 1

A poorly devised research question may affect the choice of study design, potentially lead to futile situations and, thus, hamper the chance of determining anything of clinical significance, which will then affect the potential for publication. Without devoting appropriate resources to developing the research question, the quality of the study and subsequent results may be compromised. During the initial stages of any research study, it is therefore imperative to formulate a research question that is both clinically relevant and answerable.

Research hypothesis

The primary research question should be driven by the hypothesis rather than the data. 1 , 2 That is, the research question and hypothesis should be developed before the start of the study. This sounds intuitive; however, if we take, for example, a database of information, it is potentially possible to perform multiple statistical comparisons of groups within the database to find a statistically significant association. This could then lead one to work backward from the data and develop the “question.” This is counterintuitive to the process because the question is asked specifically to then find the answer, thus collecting data along the way (i.e., in a prospective manner). Multiple statistical testing of associations from data previously collected could potentially lead to spuriously positive findings of association through chance alone. 2 Therefore, a good hypothesis must be based on a good research question at the start of a trial and, indeed, drive data collection for the study.

The research or clinical hypothesis is developed from the research question and then the main elements of the study — sampling strategy, intervention (if applicable), comparison and outcome variables — are summarized in a form that establishes the basis for testing, statistical and ultimately clinical significance. 3 For example, in a research study comparing computer-assisted acetabular component insertion versus freehand acetabular component placement in patients in need of total hip arthroplasty, the experimental group would be computer-assisted insertion and the control/conventional group would be free-hand placement. The investigative team would first state a research hypothesis. This could be expressed as a single outcome (e.g., computer-assisted acetabular component placement leads to improved functional outcome) or potentially as a complex/composite outcome; that is, more than one outcome (e.g., computer-assisted acetabular component placement leads to both improved radiographic cup placement and improved functional outcome).

However, when formally testing statistical significance, the hypothesis should be stated as a “null” hypothesis. 2 The purpose of hypothesis testing is to make an inference about the population of interest on the basis of a random sample taken from that population. The null hypothesis for the preceding research hypothesis then would be that there is no difference in mean functional outcome between the computer-assisted insertion and free-hand placement techniques. After forming the null hypothesis, the researchers would form an alternate hypothesis stating the nature of the difference, if it should appear. The alternate hypothesis would be that there is a difference in mean functional outcome between these techniques. At the end of the study, the null hypothesis is then tested statistically. If the findings of the study are not statistically significant (i.e., there is no difference in functional outcome between the groups in a statistical sense), we cannot reject the null hypothesis, whereas if the findings were significant, we can reject the null hypothesis and accept the alternate hypothesis (i.e., there is a difference in mean functional outcome between the study groups), errors in testing notwithstanding. In other words, hypothesis testing confirms or refutes the statement that the observed findings did not occur by chance alone but rather occurred because there was a true difference in outcomes between these surgical procedures. The concept of statistical hypothesis testing is complex, and the details are beyond the scope of this article.

Another important concept inherent in hypothesis testing is whether the hypotheses will be 1-sided or 2-sided. A 2-sided hypothesis states that there is a difference between the experimental group and the control group, but it does not specify in advance the expected direction of the difference. For example, we asked whether there is there an improvement in outcomes with computer-assisted surgery or whether the outcomes worse with computer-assisted surgery. We presented a 2-sided test in the above example because we did not specify the direction of the difference. A 1-sided hypothesis states a specific direction (e.g., there is an improvement in outcomes with computer-assisted surgery). A 2-sided hypothesis should be used unless there is a good justification for using a 1-sided hypothesis. As Bland and Atlman 8 stated, “One-sided hypothesis testing should never be used as a device to make a conventionally nonsignificant difference significant.”

The research hypothesis should be stated at the beginning of the study to guide the objectives for research. Whereas the investigators may state the hypothesis as being 1-sided (there is an improvement with treatment), the study and investigators must adhere to the concept of clinical equipoise. According to this principle, a clinical (or surgical) trial is ethical only if the expert community is uncertain about the relative therapeutic merits of the experimental and control groups being evaluated. 9 It means there must exist an honest and professional disagreement among expert clinicians about the preferred treatment. 9

Designing a research hypothesis is supported by a good research question and will influence the type of research design for the study. Acting on the principles of appropriate hypothesis development, the study can then confidently proceed to the development of the research objective.

Research objective

The primary objective should be coupled with the hypothesis of the study. Study objectives define the specific aims of the study and should be clearly stated in the introduction of the research protocol. 7 From our previous example and using the investigative hypothesis that there is a difference in functional outcomes between computer-assisted acetabular component placement and free-hand placement, the primary objective can be stated as follows: this study will compare the functional outcomes of computer-assisted acetabular component insertion versus free-hand placement in patients undergoing total hip arthroplasty. Note that the study objective is an active statement about how the study is going to answer the specific research question. Objectives can (and often do) state exactly which outcome measures are going to be used within their statements. They are important because they not only help guide the development of the protocol and design of study but also play a role in sample size calculations and determining the power of the study. 7 These concepts will be discussed in other articles in this series.

From the surgeon’s point of view, it is important for the study objectives to be focused on outcomes that are important to patients and clinically relevant. For example, the most methodologically sound randomized controlled trial comparing 2 techniques of distal radial fixation would have little or no clinical impact if the primary objective was to determine the effect of treatment A as compared to treatment B on intraoperative fluoroscopy time. However, if the objective was to determine the effect of treatment A as compared to treatment B on patient functional outcome at 1 year, this would have a much more significant impact on clinical decision-making. Second, more meaningful surgeon–patient discussions could ensue, incorporating patient values and preferences with the results from this study. 6 , 7 It is the precise objective and what the investigator is trying to measure that is of clinical relevance in the practical setting.

The following is an example from the literature about the relation between the research question, hypothesis and study objectives:

Study: Warden SJ, Metcalf BR, Kiss ZS, et al. Low-intensity pulsed ultrasound for chronic patellar tendinopathy: a randomized, double-blind, placebo-controlled trial. Rheumatology 2008;47:467–71.

Research question: How does low-intensity pulsed ultrasound (LIPUS) compare with a placebo device in managing the symptoms of skeletally mature patients with patellar tendinopathy?

Research hypothesis: Pain levels are reduced in patients who receive daily active-LIPUS (treatment) for 12 weeks compared with individuals who receive inactive-LIPUS (placebo).

Objective: To investigate the clinical efficacy of LIPUS in the management of patellar tendinopathy symptoms.

The development of the research question is the most important aspect of a research project. A research project can fail if the objectives and hypothesis are poorly focused and underdeveloped. Useful tips for surgical researchers are provided in Box 3 . Designing and developing an appropriate and relevant research question, hypothesis and objectives can be a difficult task. The critical appraisal of the research question used in a study is vital to the application of the findings to clinical practice. Focusing resources, time and dedication to these 3 very important tasks will help to guide a successful research project, influence interpretation of the results and affect future publication efforts.

Tips for developing research questions, hypotheses and objectives for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Develop clear and well-defined primary and secondary (if needed) objectives.
• Ensure that the research question and objectives are answerable, feasible and clinically relevant.

FINER = feasible, interesting, novel, ethical, relevant; PICOT = population (patients), intervention (for intervention studies only), comparison group, outcome of interest, time.

Competing interests: No funding was received in preparation of this paper. Dr. Bhandari was funded, in part, by a Canada Research Chair, McMaster University.

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Aug 19, 2024 • 17 minutes read

Qualitative research examples: How to unlock, rich, descriptive insights

Qualitative research uncovers in-depth user insights, but what does it look like? Here are seven methods and examples to help you get the data you need.

Armin Tanovic

Behind every what, there’s a why . Qualitative research is how you uncover that why. It enables you to connect with users and understand their thoughts, feelings, wants, needs, and pain points.

There’s many methods for conducting qualitative research, and many objectives it can help you pursue—you might want to explore ways to improve NPS scores, combat reduced customer retention, or understand (and recreate) the success behind a well-received product. The common thread? All these metrics impact your business, and qualitative research can help investigate and improve that impact.

In this article, we’ll take you through seven methods and examples of qualitative research, including when and how to use them.

Qualitative UX research made easy

Conduct qualitative research with Maze, analyze data instantly, and get rich, descriptive insights that drive decision-making.

7 Qualitative research methods: An overview

There are various qualitative UX research methods that can help you get in-depth, descriptive insights. Some are suited to specific phases of the design and development process, while others are more task-oriented.

Here’s our overview of the most common qualitative research methods. Keep reading for their use cases, and detailed examples of how to conduct them.

1. User interviews

A user interview is a one-on-one conversation between a UX researcher, designer or Product Manager and a target user to understand their thoughts, perspectives, and feelings on a product or service. User interviews are a great way to get non-numerical data on individual experiences with your product, to gain a deeper understanding of user perspectives.

Interviews can be structured, semi-structured, or unstructured . Structured interviews follow a strict interview script and can help you get answers to your planned questions, while semi and unstructured interviews are less rigid in their approach and typically lead to more spontaneous, user-centered insights.

When to use user interviews

Interviews are ideal when you want to gain an in-depth understanding of your users’ perspectives on your product or service, and why they feel a certain way.

Interviews can be used at any stage in the product design and development process, being particularly helpful during:

• The discovery phase: To better understand user needs, problems, and the context in which they use your product—revealing the best potential solutions
• The design phase: To get contextual feedback on mockups, wireframes, and prototypes, helping you pinpoint issues and the reasons behind them
• Post-launch: To assess if your product continues to meet users’ shifting expectations and understand why or why not

How to conduct user interviews: The basics

• Draft questions based on your research objectives
• Recruit relevant research participants and schedule interviews
• Conduct the interview and transcribe responses
• Analyze the interview responses to extract insights
• Use your findings to inform design, product, and business decisions

💡 A specialized user interview tool makes interviewing easier. With Maze Interview Studies , you can recruit, host, and analyze interviews all on one platform.

User interviews: A qualitative research example

Let’s say you’ve designed a recruitment platform, called Tech2Talent , that connects employers with tech talent. Before starting the design process, you want to clearly understand the pain points employers experience with existing recruitment tools'.

You draft a list of ten questions for a semi-structured interview for 15 different one-on-one interviews. As it’s semi-structured, you don’t expect to ask all the questions—the script serves as more of a guide.

One key question in your script is: “Have tech recruitment platforms helped you find the talent you need in the past?”

Most respondents answer with a resounding and passionate ‘no’ with one of them expanding:

“For our company, it’s been pretty hit or miss honestly. They let just about anyone make a profile and call themselves tech talent. It’s so hard sifting through serious candidates. I can’t see any of their achievements until I invest time setting up an interview.”

You begin to notice a pattern in your responses: recruitment tools often lack easily accessible details on talent profiles.

You’ve gained contextual feedback on why other recruitment platforms fail to solve user needs.

2. Focus groups

A focus group is a research method that involves gathering a small group of people—around five to ten users—to discuss a specific topic, such as their’ experience with your new product feature. Unlike user interviews, focus groups aim to capture the collective opinion of a wider market segment and encourage discussion among the group.

When to use focus groups

You should use focus groups when you need a deeper understanding of your users’ collective opinions. The dynamic discussion among participants can spark in-depth insights that might not emerge from regular interviews.

Focus groups can be used before, during, and after a product launch. They’re ideal:

• Throughout the problem discovery phase: To understand your user segment’s pain points and expectations, and generate product ideas
• Post-launch: To evaluate and understand the collective opinion of your product’s user experience
• When conducting market research: To grasp usage patterns, consumer perceptions, and market opportunities for your product

How to conduct focus group studies: The basics

• Draft prompts to spark conversation, or a series of questions based on your UX research objectives
• Find a group of five to ten users who are representative of your target audience (or a specific user segment) and schedule your focus group session
• Conduct the focus group by talking and listening to users, then transcribe responses
• Analyze focus group responses and extract insights
• Use your findings to inform design decisions

The number of participants can make it difficult to take notes or do manual transcriptions. We recommend using a transcription or a specialized UX research tool , such as Maze, that can automatically create ready-to-share reports and highlight key user insights.

Focus groups: A qualitative research example

You’re a UX researcher at FitMe , a fitness app that creates customized daily workouts for gym-goers. Unlike many other apps, FitMe takes into account the previous day’s workout and aims to create one that allows users to effectively rest different muscles.

However, FitMe has an issue. Users are generating workouts but not completing them. They’re accessing the app, taking the necessary steps to get a workout for the day, but quitting at the last hurdle.

Time to talk to users.

You organize a focus group to get to the root of the drop-off issue. You invite five existing users, all of whom have dropped off at the exact point you’re investigating, and ask them questions to uncover why.

A dialog develops:

Participant 1: “Sometimes I’ll get a workout that I just don’t want to do. Sure, it’s a good workout—but I just don’t want to physically do it. I just do my own thing when that happens.”

Participant 2: “Same here, some of them are so boring. I go to the gym because I love it. It’s an escape.”

Participant 3: “Right?! I get that the app generates the best one for me on that specific day, but I wish I could get a couple of options.”

Participant 4: “I’m the same, there are some exercises I just refuse to do. I’m not coming to the gym to do things I dislike.”

Conducting the focus groups and reviewing the transcripts, you realize that users want options. A workout that works for one gym-goer doesn’t necessarily work for the next.

A possible solution? Adding the option to generate a new workout (that still considers previous workouts)and the ability to blacklist certain exercises, like burpees.

3. Ethnographic research

Ethnographic research is a research method that involves observing and interacting with users in a real-life environment. By studying users in their natural habitat, you can understand how your product fits into their daily lives.

Ethnographic research can be active or passive. Active ethnographic research entails engaging with users in their natural environment and then following up with methods like interviews. Passive ethnographic research involves letting the user interact with the product while you note your observations.

When to use ethnographic research

Ethnographic research is best suited when you want rich insights into the context and environment in which users interact with your product. Keep in mind that you can conduct ethnographic research throughout the entire product design and development process —from problem discovery to post-launch. However, it’s mostly done early in the process:

• Early concept development: To gain an understanding of your user's day-to-day environment. Observe how they complete tasks and the pain points they encounter. The unique demands of their everyday lives will inform how to design your product.
• Initial design phase: Even if you have a firm grasp of the user’s environment, you still need to put your solution to the test. Conducting ethnographic research with your users interacting with your prototype puts theory into practice.

How to conduct ethnographic research:

• Recruit users who are reflective of your audience
• Meet with them in their natural environment, and tell them to behave as they usually would
• Take down field notes as they interact with your product
• Engage with your users, ask questions, or host an in-depth interview if you’re doing an active ethnographic study
• Collect all your data and analyze it for insights

While ethnographic studies provide a comprehensive view of what potential users actually do, they are resource-intensive and logistically difficult. A common alternative is diary studies. Like ethnographic research, diary studies examine how users interact with your product in their day-to-day, but the data is self-reported by participants.

⚙️ Recruiting participants proving tough and time-consuming? Maze Panel makes it easy, with 400+ filters to find your ideal participants from a pool of 3 million participants.

Ethnographic research: A qualitative research example

You're a UX researcher for a project management platform called ProFlow , and you’re conducting an ethnographic study of the project creation process with key users, including a startup’s COO.

The first thing you notice is that the COO is rushing while navigating the platform. You also take note of the 46 tabs and Zoom calls opened on their monitor. Their attention is divided, and they let out an exasperated sigh as they repeatedly hit “refresh” on your website’s onboarding interface.

You conclude the session with an interview and ask, “How easy or difficult did you find using ProFlow to coordinate a project?”

The COO answers: “Look, the whole reason we turn to project platforms is because we need to be quick on our feet. I’m doing a million things so I need the process to be fast and simple. The actual project management is good, but creating projects and setting up tables is way too complicated.”

You realize that ProFlow ’s project creation process takes way too much time for professionals working in fast-paced, dynamic environments. To solve the issue, propose a quick-create option that enables them to move ahead with the basics instead of requiring in-depth project details.

4. Qualitative observation

Qualitative observation is a similar method to ethnographic research, though not as deep. It involves observing your users in a natural or controlled environment and taking notes as they interact with a product. However, be sure not to interrupt them, as this compromises the integrity of the study and turns it into active ethnographic research.

When to qualitative observation

Qualitative observation is best when you want to record how users interact with your product without anyone interfering. Much like ethnographic research, observation is best done during:

• Early concept development: To help you understand your users' daily lives, how they complete tasks, and the problems they deal with. The observations you collect in these instances will help you define a concept for your product.
• Initial design phase: Observing how users deal with your prototype helps you test if they can easily interact with it in their daily environments

How to conduct qualitative observation:

• Recruit users who regularly use your product
• Meet with users in either their natural environment, such as their office, or within a controlled environment, such as a lab
• Observe them and take down field notes based on what you notice

Qualitative observation: An qualitative research example

You’re conducting UX research for Stackbuilder , an app that connects businesses with tools ideal for their needs and budgets. To determine if your app is easy to use for industry professionals, you decide to conduct an observation study.

Sitting in with the participant, you notice they breeze past the onboarding process, quickly creating an account for their company. Yet, after specifying their company’s budget, they suddenly slow down. They open links to each tool’s individual page, confusingly switching from one tab to another. They let out a sigh as they read through each website.

Conducting your observation study, you realize that users find it difficult to extract information from each tool’s website. Based on your field notes, you suggest including a bullet-point summary of each tool directly on your platform.

5. Case study research

Case studies are a UX research method that provides comprehensive and contextual insights into a real-world case over a long period of time. They typically include a range of other qualitative research methods, like interviews, observations, and ethnographic research. A case study allows you to form an in-depth analysis of how people use your product, helping you uncover nuanced differences between your users.

When to use case studies

Case studies are best when your product involves complex interactions that need to be tracked over a longer period or through in-depth analysis. You can also use case studies when your product is innovative, and there’s little existing data on how users interact with it.

As for specific phases in the product design and development process:

• Initial design phase: Case studies can help you rigorously test for product issues and the reasons behind them, giving you in-depth feedback on everything between user motivations, friction points, and usability issues
• Post-launch phase: Continuing with case studies after launch can give you ongoing feedback on how users interact with the product in their day-to-day lives. These insights ensure you can meet shifting user expectations with product updates and future iterations

How to conduct case studies:

• Outline an objective for your case study such as examining specific user tasks or the overall user journey
• Select qualitative research methods such as interviews, ethnographic studies, or observations
• Collect and analyze your data for comprehensive insights
• Include your findings in a report with proposed solutions

Case study research: A qualitative research example

Your team has recently launched Pulse , a platform that analyzes social media posts to identify rising digital marketing trends. Pulse has been on the market for a year, and you want to better understand how it helps small businesses create successful campaigns.

To conduct your case study, you begin with a series of interviews to understand user expectations, ethnographic research sessions, and focus groups. After sorting responses and observations into common themes you notice a main recurring pattern. Users have trouble interpreting the data from their dashboards, making it difficult to identify which trends to follow.

With your synthesized insights, you create a report with detailed narratives of individual user experiences, common themes and issues, and recommendations for addressing user friction points.

Some of your proposed solutions include creating intuitive graphs and summaries for each trend study. This makes it easier for users to understand trends and implement strategic changes in their campaigns.

6. Secondary research

Secondary research is a research method that involves collecting and analyzing documents, records, and reviews that provide you with contextual data on your topic. You’re not connecting with participants directly, but rather accessing pre-existing available data. For example, you can pull out insights from your UX research repository to reexamine how they apply to your new UX research objective.

Strictly speaking, it can be both qualitative and quantitative—but today we focus on its qualitative application.

When to use secondary research

Record keeping is particularly useful when you need supplemental insights to complement, validate, or compare current research findings. It helps you analyze shifting trends amongst your users across a specific period. Some other scenarios where you need record keeping include:

• Initial discovery or exploration phase: Secondary research can help you quickly gather background information and data to understand the broader context of a market
• Design and development phase: See what solutions are working in other contexts for an idea of how to build yours

Secondary research is especially valuable when your team faces budget constraints, tight deadlines, or limited resources. Through review mining and collecting older findings, you can uncover useful insights that drive decision-making throughout the product design and development process.

How to conduct secondary research:

• Outline your UX research objective
• Identify potential data sources for information on your product, market, or target audience. Some of these sources can include: a. Review websites like Capterra and G2 b. Social media channels c. Customer service logs and disputes d. Website reviews e. Reports and insights from previous research studies f. Industry trends g. Information on competitors
• Analyze your data by identifying recurring patterns and themes for insights

Secondary research: A qualitative research example

SafeSurf is a cybersecurity platform that offers threat detection, security audits, and real-time reports. After conducting multiple rounds of testing, you need a quick and easy way to identify remaining usability issues. Instead of conducting another resource-intensive method, you opt for social listening and data mining for your secondary research.

Browsing through your company’s X, you identify a recurring theme: many users without a background in tech find SafeSurf ’s reports too technical and difficult to read. Users struggle with understanding what to do if their networks are breached.

After checking your other social media channels and review sites, the issue pops up again.

With your gathered insights, your team settles on introducing a simplified version of reports, including clear summaries, takeaways, and step-by-step protocols for ensuring security.

By conducting secondary research, you’ve uncovered a major usability issue—all without spending large amounts of time and resources to connect with your users.

7. Open-ended surveys

Open-ended surveys are a type of unmoderated UX research method that involves asking users to answer a list of qualitative research questions designed to uncover their attitudes, expectations, and needs regarding your service or product. Open-ended surveys allow users to give in-depth, nuanced, and contextual responses.

When to use open-ended surveys

User surveys are an effective qualitative research method for reaching a large number of users. You can use them at any stage of the design and product development process, but they’re particularly useful:

• When you’re conducting generative research : Open-ended surveys allow you to reach a wide range of users, making them especially useful during initial research phases when you need broad insights into user experiences
• When you need to understand customer satisfaction: Open-ended customer satisfaction surveys help you uncover why your users might be dissatisfied with your product, helping you find the root cause of their negative experiences
• In combination with close-ended surveys: Get a combination of numerical, statistical insights and rich descriptive feedback. You’ll know what a specific percentage of your users think and why they think it.

How to conduct open-ended surveys:

• Design your survey and draft out a list of survey questions
• Distribute your surveys to respondents
• Analyze survey participant responses for key themes and patterns
• Use your findings to inform your design process

Open-ended surveys: A qualitative research example

You're a UX researcher for RouteReader , a comprehensive logistics platform that allows users to conduct shipment tracking and route planning. Recently, you’ve launched a new predictive analytics feature that allows users to quickly identify and prepare for supply chain disruptions.

To better understand if users find the new feature helpful, you create an open-ended, in-app survey.

The questions you ask your users:

• “What has been your experience with our new predictive analytics feature?"
• “Do you find it easy or difficult to rework your routes based on our predictive suggestions?”
• “Does the predictive analytics feature make planning routes easier? Why or why not?”

Most of the responses are positive. Users report using the predictive analytics feature to make last-minute adjustments to their route plans, and some even rely on it regularly. However, a few users find the feature hard to notice, making it difficult to adjust their routes on time.

To ensure users have supply chain insights on time, you integrate the new feature into each interface so users can easily spot important information and adjust their routes accordingly.

💡 Surveys are a lot easier with a quality survey tool. Maze’s Feedback Surveys solution has all you need to ensure your surveys get the insights you need—including AI-powered follow-up and automated reports.

Qualitative research vs. quantitative research: What’s the difference?

Alongside qualitative research approaches, UX teams also use quantitative research methods. Despite the similar names, the two are very different.

Here are some of the key differences between qualitative research and quantitative research .

Before selecting either qualitative or quantitative methods, first identify what you want to achieve with your UX research project. As a general rule of thumb, think qualitative data collection for in-depth understanding and quantitative studies for measurement and validation.

Conduct qualitative research with Maze

You’ll often find that knowing the what is pointless without understanding the accompanying why . Qualitative research helps you uncover your why.

So, what about how —how do you identify your 'what' and your 'why'?

The answer is with a user research tool like Maze.

Maze is the leading user research platform that lets you organize, conduct, and analyze both qualitative and quantitative research studies—all from one place. Its wide variety of UX research methods and advanced AI capabilities help you get the insights you need to build the right products and experiences faster.

Frequently asked questions about qualitative research examples

What is qualitative research?

Qualitative research is a research method that aims to provide contextual, descriptive, and non-numerical insights on a specific issue. Qualitative research methods like interviews, case studies, and ethnographic studies allow you to uncover the reasoning behind your user’s attitudes and opinions.

Can a study be both qualitative and quantitative?

Absolutely! You can use mixed methods in your research design, which combines qualitative and quantitative approaches to gain both descriptive and statistical insights.

For example, user surveys can have both close-ended and open-ended questions, providing comprehensive data like percentages of user views and descriptive reasoning behind their answers.

Is qualitative or quantitative research better?

The choice between qualitative and quantitative research depends upon your research goals and objectives.

Qualitative research methods are better suited when you want to understand the complexities of your user’s problems and uncover the underlying motives beneath their thoughts, feelings, and behaviors. Quantitative research excels in giving you numerical data, helping you gain a statistical view of your user's attitudes, identifying trends, and making predictions.

What are some approaches to qualitative research?

There are many approaches to qualitative studies. An approach is the underlying theory behind a method, and a method is a way of implementing the approach. Here are some approaches to qualitative research:

• Grounded theory: Researchers study a topic and develop theories inductively
• Phenomenological research: Researchers study a phenomenon through the lived experiences of those involved
• Ethnography: Researchers immerse themselves in organizations to understand how they operate

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• Knowledge Base
• Starting the research process
• Writing Strong Research Questions | Criteria & Examples

Writing Strong Research Questions | Criteria & Examples

Published on October 26, 2022 by Shona McCombes . Revised on November 21, 2023.

A research question pinpoints exactly what you want to find out in your work. A good research question is essential to guide your research paper , dissertation , or thesis .

All research questions should be:

• Focused on a single problem or issue
• Researchable using primary and/or secondary sources
• Feasible to answer within the timeframe and practical constraints
• Specific enough to answer thoroughly
• Complex enough to develop the answer over the space of a paper or thesis
• Relevant to your field of study and/or society more broadly

Table of contents

How to write a research question, what makes a strong research question, using sub-questions to strengthen your main research question, research questions quiz, other interesting articles, frequently asked questions about research questions.

You can follow these steps to develop a strong research question:

• Choose your topic
• Do some preliminary reading about the current state of the field
• Narrow your focus to a specific niche
• Identify the research problem that you will address

The way you frame your question depends on what your research aims to achieve. The table below shows some examples of how you might formulate questions for different purposes.

Using your research problem to develop your research question

Note that while most research questions can be answered with various types of research , the way you frame your question should help determine your choices.

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Research questions anchor your whole project, so it’s important to spend some time refining them. The criteria below can help you evaluate the strength of your research question.

Focused and researchable

Feasible and specific

Complex and arguable

Relevant and original

Chances are that your main research question likely can’t be answered all at once. That’s why sub-questions are important: they allow you to answer your main question in a step-by-step manner.

Good sub-questions should be:

• Less complex than the main question
• Focused only on 1 type of research
• Presented in a logical order

Here are a few examples of descriptive and framing questions:

• Descriptive: According to current government arguments, how should a European bank tax be implemented?
• Descriptive: Which countries have a bank tax/levy on financial transactions?
• Framing: How should a bank tax/levy on financial transactions look at a European level?

Keep in mind that sub-questions are by no means mandatory. They should only be asked if you need the findings to answer your main question. If your main question is simple enough to stand on its own, it’s okay to skip the sub-question part. As a rule of thumb, the more complex your subject, the more sub-questions you’ll need.

Try to limit yourself to 4 or 5 sub-questions, maximum. If you feel you need more than this, it may be indication that your main research question is not sufficiently specific. In this case, it’s is better to revisit your problem statement and try to tighten your main question up.

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If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .

A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis —a prediction that will be confirmed or disproved by your research.

As you cannot possibly read every source related to your topic, it’s important to evaluate sources to assess their relevance. Use preliminary evaluation to determine whether a source is worth examining in more depth.

This involves:

• Reading abstracts , prefaces, introductions , and conclusions
• Looking at the table of contents to determine the scope of the work
• Consulting the index for key terms or the names of important scholars

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (“ x affects y because …”).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses . In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

Formulating a main research question can be a difficult task. Overall, your question should contribute to solving the problem that you have defined in your problem statement .

However, it should also fulfill criteria in three main areas:

• Researchability
• Feasibility and specificity
• Relevance and originality

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Anthropology students present their research in poetry, plays and op-eds in this course

Professor of Anthropology, University of California, Irvine

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Roxanne Varzi received funding 20 years ago from fulbright in 2000 and a woodrow wilson in 2002-2004

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Uncommon Courses is an occasional series from The Conversation U.S. highlighting unconventional approaches to teaching.

Title of course:

Multimodal Anthropology

What prompted the idea for the course?

“Multimodal” simply means using more than academic written text to present research findings.

My first multimodal science project was a third grade project on humpback whales, which was inspired by a vinyl LP recording of their “songs” that National Geographic mailed out to subscribers.

In addition to my written report, I included the whale recording and photographs from National Geographic. I then added a popular piece of material culture by tying the project together with white shoelaces dotted with tiny blue whales.

As an anthropologist today, I use fiction, plays, poetry, sound and film – among other media – to present my findings. To other academics, I’ve often had to defend my methods. But students tell me they love it and want to do the same.

What does the course explore?

We explore nontraditional ways to present research and discuss why a particular form may or may not work for a particular subject. For example, a student could use the sound of bombs for a research presentation about war, rather than showing violent images, which can demean victims . For a presentation about Iranian underground theater, a fictional story about clandestine rehearsals and relationships , rather than an academic book, could be used to protect subjects’ privacy.

Students learn basic ethnographic research methods, including fly-on-the-wall observation and interviewing. We go out on exploratory research trips around campus to observe, take notes and ask questions. When they choose the topic of their final research projects, they take whatever data they gathered in a given week and present it in different formats, including film, poetry and op-eds.

Why is this course relevant now?

Practicing multimodal anthropology helps students explore their own strengths and grow their confidence. Not everyone writes well, but some are gifted photographers. Educators have lost the interest of talented students by consistently demanding that research always be presented in term papers.

It also makes the material more accessible to a larger and more diverse audience. Take sound – or even audiobooks – for example. The sound of humpback whales or melting glaciers allows people to experience a place that most cannot actually visit. Audiobooks or spoken-word projects allow readers with vision impairments and reading disabilities to access the research.

What’s a critical lesson from the course?

There is never just one way to do anything. Students will be successful and can facilitate change in the world by playing to their strengths and by collaborating rather than competing with their peers. They are not their test scores or final grades, but rather a product of the experiences they have with their projects and each other along the way.

What materials does the course feature?

I use my own work to explain the process of multimodal anthropology – how and why I did what I did and whether it was successful. I also bring in other guest lecturers, including multimedia artist Kurosh ValaNejad , who put the history of the American-led coup d’etat in Iran in 1953 into a video game .

I am currently developing my own multimodal curriculum in the form of a murder mystery series in which each book explores a different way to present research. My first book, “ Death in a Nutshell: An Anthropology Whodunnit ,” explores visual anthropology – the study of images in culture and the use of images to present culture – and dyslexia.

What will the course prepare students to do?

The course prepares students to practice and learn about multiple research methods. Even if they never write another poem again, studying poetry as an anthropologist sharpens their descriptive abilities. It also leads to useful discussions, like whether poetry, as Plato claims , is too beautiful, alluring and potentially deceptive to be used in what people like to think of as an objective science.

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• Published: 19 August 2024

A many-objective evolutionary algorithm based on three states for solving many-objective optimization problem

• Jiale Zhao 1 , 4 ,
• Huijie Zhang 3 ,
• Huanhuan Yu 2 , 4 ,
• Hansheng Fei 2 , 4 ,
• Xiangdang Huang 2 , 4 &
• Qiuling Yang 2 , 4  

Scientific Reports volume  14 , Article number:  19140 ( 2024 ) Cite this article

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In recent years, researchers have taken the many-objective optimization algorithm, which can optimize 5, 8, 10, 15, 20 objective functions simultaneously, as a new research topic. However, the current research on many-objective optimization technology also encounters some challenges. For example: Pareto resistance phenomenon, difficult diversity maintenance. Based on the above problems, this paper proposes a many-objective evolutionary algorithm based on three states (MOEA/TS). Firstly, a feature extraction operator is proposed. It can extract the features of the high-quality solution set, and then assist the evolution of the current individual. Secondly, based on Pareto front layer, the concept of “individual importance degree” is proposed. The importance degree of an individual can reflect the importance of the individual in the same Pareto front layer, so as to further distinguish the advantages and disadvantages of different individuals in the same front layer. Then, a repulsion field method is proposed. The diversity of the population in the objective space is maintained by the repulsion field, so that the population can be evenly distributed on the real Pareto front. Finally, a new concurrent algorithm framework is designed. In the algorithm framework, the algorithm is divided into three states, and each state focuses on a specific task. The population can switch freely among these three states according to its own evolution. The MOEA/TS algorithm is compared with 7 advanced many-objective optimization algorithms. The experimental results show that the MOEA/TS algorithm is more competitive in many-objective optimization problems.

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Introduction.

In reality, many optimization problems involve multiple conflicting objectives, such as the design of urban public transport routes 1 , production scheduling 2 , securities portfolio management 3 and so on. These types of optimization problems are called multi-objective optimization problems (MOPs). This means that there is no one solution to make all the objectives reach the optimum simultaneously, that is, the optimization of one objective may lead to the deterioration of other objectives 4 , 5 . Consequently, the solutions of MOPs are usually a set of compromise solutions that weigh all objectives. The definition of MOPs is as follows:

Among them, \(f\left(x\right)\) is the m-dimensional objective vector, which contains m conflicting objective functions; \({f}_{i}\left(x\right)\) represents the i-th objective function; x represents the n-dimensional decision variable; \(\Omega \) represents decision space; R m represents the objective space.

In the field of multi-objective optimization, problems with 2 or 3 optimization objectives are called general multi-objective optimization problems (GMOPs). Problems with more than 3 optimization objectives are called many-objective optimization problems (MaOPs) 6 , 7 , 8 . GMOPs aren’t the focus of our attention, as there have been many reports about GMOPs 9 , 10 . On the contrary, MaOPs are the focus of our attention, as there are still some challenges to be solved. The fundamental difference between GMOPs and MaOPs is the number of optimization objectives. Assuming that the number of optimization objectives is m, the probability that one individual dominates another is \(1/{2}^{m-1}\) in theory 11 , 12 . This means that with the increase of the number of optimization objectives, traditional Pareto dominance will fail, Pareto resistance will occur, and most multi-objective optimization algorithms will lose selection pressure in terms of convergence.

In recent years, with the research and exploration of MaOPs, many-objective optimization technology has been developed to a certain extent, and basically 4 mainstream many-objective optimization algorithms have been formed 13 . The first is many-objective optimization algorithm based on dominance. The algorithm modifies the definition of traditional Pareto domination by domination relaxation technique to enhance the selection pressure of the algorithm in terms of convergence. \(\alpha\) -Dominance, \(\upepsilon \) -Dominance and Cone \(\upepsilon \) -Dominance are all common domination relaxation techniques. Compared with traditional Pareto dominance, the effectiveness of dominance relaxation technology has been reported in many works. Therefore, dominance relaxation technology has been widely used to solve MaOPs. However, the current domination relaxation technique also faces two problems: (1) With the increase of the number of optimization objectives, the effect of the domination relaxation technique is getting worse and worse; (2) The domination relaxation technique tends to make the population converge to a certain sub-region of the real Pareto front (PF).

The second is many-objective optimization algorithm based on index. The algorithm guides the selection and evolution of the population by integrating convergence and diversity into one index (such as IGD, HV). Its representative work includes: HypE, MaOEA/IGD, SMS-EMOA. However, the algorithm faces some problems when it is used to solve MaOPs, such as complex index calculation, difficult selection of reference point or reference PF.

The third is many-objective optimization algorithm based on decomposition. The algorithm transforms MaOPs into several single-objective optimization sub-problems through an aggregation function, and then drives the individuals in the neighborhood to update by neighborhood strategy, finally realizes the evolution of the whole population. Its representative work includes: MOEA/D, MOEA/D-D, MOEA/D-DU. However, many-objective optimization algorithm based on decomposition is only suitable for MaOPs with regular PF (such as the DTLZ1 problem). When dealing with MaOPs with irregular PF, many-objective optimization algorithm based on decomposition often performs poorly.

The fourth is many-objective optimization algorithm based on hybrid strategy. The algorithm adopts different search strategies in different environments (different stages or different sub-populations), and uses the advantages of their respective search strategies to deal with complex MaOPs. Its representative work includes: AHM, eMOFEOA, CPSO. In many reports, many-objective optimization algorithm based on hybrid strategy is more suitable for solving MaOPs.

According to the above analysis, this paper considers using the many-objective optimization algorithm based on hybrid strategy, and further proposes the many-objective evolutionary algorithm based on three states (MOEA/TS). The innovations and contributions of this paper are as follows: (1) A feature extraction operator is proposed. The feature extraction operator is a feature extractor, which can extract the features of the high-quality solution set, and then assist the evolution of the current individual. (2) Based on the Pareto front layer, the concept of “individual importance degree” is proposed. The importance degree of an individual can reflect the importance of the individual in the same Pareto front layer, so as to further distinguish the advantages and disadvantages of different individuals in the same front layer, and effectively solve the phenomenon of Pareto resistance. (3) A repulsion field method is proposed. The repulsion field is used to maintain the diversity of the population in the objective space, so that the population can be evenly distributed on the real PF. (4) Design a new concurrent algorithm framework. In the framework, the algorithm is divided into three states, and each state focuses on a specific task. The population can freely switch among these three states according to its own evolution.

The remainder of this paper is organized as follows: Sect. " Preparatory work " introduces the basic definition, related work and research motivation. Sect. " Basic definition " introduces each part of the MOEA/TS algorithm in detail. Sect. " Related work " introduces the test results of MOEA/TS algorithm and 7 advanced many-objective optimization algorithms on various test problems, and then analyzes and summarizes them according to the test results. Sect. " Many-objective optimization algorithm based on dominance "summarizes this article and looks forward to future work.

Preparatory work

Basic definition.

In this section, we will introduce some basic definitions related to many-objective optimization technology.

Definition of dominance: if solution x isn’t worse than solution y in all objectives and solution x is better than solution y in at least one objective, it is said that x dominates y . That is, if \(\forall i\in \left\{\text{1,2},3,...,m\right\}\) satisfies \({f}_{i}\left(x\right)\le {f}_{i}\left(y\right)\) and \(\exists j\in \left\{\text{1,2},3,...,m\right\}\) satisfies \({f}_{j}\left(x\right)<{f}_{j}\left(y\right)\) , it is said that x dominates y .

Definition of non-dominated solution: if there are no solutions that can dominate x in the decision space, then x is called a Pareto optimal solution or a non-dominated solution. That is, if \(\nexists {x}^{*}\in \Omega \) makes x* dominate x , then x is called a Pareto optimal solution or a non-dominated solution.

Definition of Pareto optimal solution set: the set composed of Pareto optimal solutions is called the Pareto optimal solution set (PS). The mathematical description of PS is as follows:

Definition of Pareto front: the mapping of PS in the objective space is called Pareto front (PF). The mathematical description of PF is as follows:

The goal of the many-objective optimization technology is to find a set of non-dominated solutions that are close to the real PF (convergence) and make them well distributed on the real PF (diversity).

Related work

In recent years, many scholars have conducted in-depth research and exploration in the many-objective optimization technology.

Many-objective optimization algorithm based on dominance

Considering the limitations of Pareto dominance relationship in high-dimensional objective space, Zhou et al 14 proposed a many-objective optimization algorithm based on dominance relation selection. Firstly, they introduced an angle domination relationship with higher selection pressure based on the traditional Pareto domination relationship, and designed a new dominance selection strategy. Additionally, they proposed an angle-based individual distribution method to ensure even population distribution in the objective space. The algorithm shows strong competitiveness in solving MaOPs. Wang et al 15 believed that as the number of objectives increased, the traditional dominance relationship would become invalid. Therefore, they proposed a modified dominance relation. That is, they used penalty-based adaptive matrix regions to assist the traditional dominance relationship. Further, for MaOPs with irregular Pareto fronts, they introduced a population-based adaptive adjustment method to replace the predefined weight vector. On this basis, for MaOPs, they developed a many-objective optimization algorithm based on modified dominance relation and adaptive adjustment method. Zhang et al 16 believed that the current many-objective optimization algorithms focused too much on convergence, which would cause the population to converge to a certain sub-region of the real Pareto front. In order to solve this problem, they proposed a many-objective optimization algorithm based on double distance domination. In this algorithm, double distance can not only measure the convergence of the algorithm to adapt to different Pareto fronts, but also combine angle-based niche technology to emphasize the diversity of the algorithm. In addition, they also designed a special mutation operator. This operator can generate high-quality individuals in sparse areas to improve the diversity of the algorithm.

Many-objective optimization algorithm based on index

Aiming at the high complexity problem of hypervolume computation, Shang et al 17 proposed a new multi-objective evolutionary algorithm (MOEA) based on R2 index, namely the R2HCA-EMOA algorithm. The core idea of this algorithm is to use R2 index variables to approximate the contribution of hypervolume. The basic framework of the proposed algorithm is similar to that of SMS-EMOA. In order to improve the calculation efficiency of the algorithm, the utility tensor structure is introduced to calculate R2 index variables. In addition, the normalization mechanism is incorporated into the R2HCA-EMOA algorithm to improve its performance. Zhang et al 18 believed that the loss of selection pressure was the core reason for the poor performance of the algorithm. In order to solve this problem, they proposed a many-objective optimization algorithm based on fitness evaluation and hierarchical grouping. The fitness evaluation method combined the convergence measure based on the cos function and the diversity measure based on angle to create the selection pressure of convergence and diversity. In order to further strengthen the selection pressure, they proposed a hierarchical grouping strategy. Firstly, individuals are divided into different layers by front index, and then individuals in the same layer are divided into different groups by R2 index. Although some indexes can approximate the contribution of HV, However, Nan et al 19 believed that the key of performance evaluation was to find the worst solution rather than accurately approaching the HV value of each solution. In order to improve the ability to identify the worst solution, they proposed a two-stage R2 index evaluation method. In the first stage, the R2 indexes of all individuals are roughly evaluated to select some candidate solutions. In the second stage, these candidate solutions are accurately evaluated. Finally, they proposed a many-objective optimization algorithm based on the two-stage R2 index.

Many-objective optimization algorithm based on decomposition

In order to balance the convergence and diversity of the decomposition-based algorithm and reduce its dependence on the real PF direction, Wu et al 20 developed a many-objective optimization algorithm based on antagonistic decomposition method. This method utilizes the complementary characteristics of different sub-problems in a single example. Specifically, two populations are co-evolved by two sub-problems with different contours and opposite search directions. In order to avoid allocating redundant computing resources to the same area of PF, two populations are matched into one-to-one pairing according to their working areas on PF. In mating selection, each solution pair can only contribute one parent at most. In order to improve the performance of decomposition-based algorithms, Fan et al 21 proposed a differential multi-objective optimization algorithm based on decomposition. Firstly, they designed a neighborhood intimacy factor to improve the diversity of the algorithm based on the characteristics of neighborhood search. Then, they introduced a Gaussian mutation operator with dynamic step size to enhance the algorithm’s ability to escape from local optimal regions and improve convergence. Finally, they combined a difference strategy with the decomposition-based multi-objective optimization algorithm to further strengthen its evolutionary ability. Peng et al 22 believed that data dimensionality reduction could be applied to the objective space. Based on this consideration, they proposed a many-objective optimization algorithm based on projection. Firstly, they used the idea of data dimensionality reduction and spatial decomposition to divide the objective space into projection plane and free dimension. Then, a double elite strategy was used to maintain the balance between convergence and diversity of the algorithm. Finally, the algorithm based on decomposition was used as the algorithm of free dimension to solve MaOPs.

Many-objective optimization algorithm based on hybrid strategy

Aiming at convergence problem and diversity problem of the algorithm, Sun et al 23 proposed a many-objective optimization algorithm based on two independent stages. The algorithm deals with convergence and diversity problems in two independent and successive stages. Firstly, they introduced a non-dominated dynamic weight aggregation method, which is capable of identifying the Pareto optimal solutions of MaOPs. Then, they used these solutions to learn the Pareto optimal subspace in order to solve the convergence problem. Finally, the diversity problem was solved by using reference lines in the Pareto optimal subspace. Considering the advantages of the multi-objective and multi-population (MPMO) framework in solving MaOPs, Yang et al 24 proposed an algorithm based on the MPMO framework. The algorithm adopts the deviation sorting (BS) method to solve MaOPs, so as to obtain good convergence and diversity. In terms of convergence, the BS method is applied to each population in the MPMO framework, and the effect of non-dominant sorting is enhanced by the optimization objectives of the corresponding population. In terms of diversity, the maintenance method based on reference vector is used to save the diversity solutions. Aiming at the five-objective job shop scheduling problem (JSSP), Liu et al 25 proposed a new genetic algorithm based on the MPMO framework. Firstly, five populations are used to optimize five objectives, respectively. Secondly, in order to prevent each population from focusing only on its corresponding single objective, an archive sharing technology (AST) is proposed to store the elite solutions collected from five populations, so that the population can obtain the optimization information of other objectives from the archive. Thirdly, the archive updating strategy (AUS) is proposed to further improve the quality of the solutions in the archive.

Research motivation

Based on the related work, we believe that there are still the following problems in the current many-objective optimization technology:

(1) The diversity and convergence of the algorithm are difficult to balance. Most algorithms can’t coordinate the balance between them well, and they either emphasize convergence or diversity too much, which leads to poor quality of the non-dominated solution set.

(2) It is difficult to maintain the convergence of the algorithm. When the number of optimization objectives is large, the algorithm will produce Pareto resistance, and the traditional Pareto dominance may fail.

(3) It is difficult to maintain the diversity of the algorithm. Especially when the real PF is complex or the latitude of the objective space is high, individuals may have the clustering effect, and the population may not be evenly distributed on the real PF.

(4) The evolution efficiency of the algorithm is low. The traditional evolution operators have strong randomness and low evolution efficiency, and aren’t suitable for dealing with MaOPs.

Therefore, solving these problems and providing a good many-objective optimization algorithm constitute the research motivation of this paper.

For problem 1, some work attempts to separate the convergence optimization and diversity optimization of the algorithm, thus designing a concurrent algorithm architecture. Concurrent algorithm architecture means that only one of convergence or diversity is considered in one iteration instead of considering both convergence and diversity simultaneously. In order to solve GMOPs, Professor Ye Tian 26 tried to design a concurrent algorithm architecture and proposed the MSEA algorithm, and the experimental results were satisfactory. Therefore, it seems to be a feasible path to solve MaOPs by using concurrent algorithm architecture. However, recent research 23 shows that in MaOPs, the concurrent algorithm architecture seems to be unstable, and the experimental results fluctuate greatly (such as MaOEA/IT algorithm). Because when the algorithm only considers the convergence of the population, it often affects the diversity of the population; Similarly, when the algorithm only considers the diversity of the population, it often affects the convergence of the population. If a coordination intermediary can be added to the concurrent algorithm architecture to alleviate the contradiction between diversity and convergence, the concurrent algorithm architecture will become stable and its superiority will be truly reflected. Based on this motivation, this paper proposes a new concurrent algorithm framework. In the new algorithm framework, the algorithm is divided into three states, namely, convergence maintenance state, diversity maintenance state and coordination state. Each state focuses on a specific task. That is, the convergence maintenance state is responsible for improving the population convergence; Diversity maintenance state is responsible for improving population diversity; the coordination state is responsible for coordinating the contradiction between diversity and convergence. The population can freely switch among these three states according to its own evolution.

For problem 2, some scholars try to modify the definition of traditional Pareto dominance by using dominance relaxation technology to enhance the selection pressure of the algorithm in terms of convergence. However, with the increase of the number of optimization objectives, the effect of dominance relaxation technology is getting worse and worse. They only focus on the modification of Pareto domination definition, but ignore the difference between objective values. If we can distinguish the importance of different individuals by using the difference between the objective values, we can further create the selection pressure of the algorithm in terms of convergence, and finally Pareto resistance will be eliminated. Therefore, based on Pareto front layer, this paper proposes the concept of “individual importance degree”. The importance degree of an individual can reflect the importance of the individual in the same Pareto front layer, so as to further distinguish the advantages and disadvantages of different individuals in the same front layer, and effectively solve the phenomenon of Pareto resistance. Obviously, compared with domination relaxation technique, individual importance degree has greater advantages.

For problem 3, the traditional diversity maintenance technology isn’t suitable for high-dimensional objective space. For instance: the niche method, the density evaluation method, and the weight vector method. In the field of microphysics, when the distance between particles is too close, repulsion will push the particles away from their neighbors. On the contrary, when the distance between particles is too great, the repulsion will decrease and the particles tend to be close to the neighboring particles. This way makes the distribution of particles present a state of mutual coordination. Based on the characteristics of particle distribution, a repulsion field method is proposed in this paper. The repulsion field is used to maintain the diversity of the population in the objective space, so that the population can be evenly distributed on the real PF.

For problem 4, traditional evolution operators aren’t suitable for dealing with MaOPs. Because traditional evolution operators have strong randomness and low evolution efficiency. For instance: the binary crossover operator, the polynomial mutation operator, and the differential evolution operator. In principal component analysis, the decomposition of the covariance matrix and correlation matrix is a very important step. By decomposing the covariance matrix or the correlation matrix, we can obtain a set of orthogonal bases. These orthogonal bases are the most important features of the original data 27 . Therefore, this paper designs a feature extraction operator based on Cholesky decomposition 28 . The feature extraction operator can be understood as a feature extractor. It can extract the features of the high-quality solution set, and then assist the evolution of the current individual. Obviously, compared with traditional evolution operators, the feature extraction operator has higher evolution efficiency.

MOEA/TS algorithm

Feature extraction operator.

The feature extraction operator is a feature extractor, which can extract the features of the high-quality solution set, and then assist the evolution of the current individual. The workflow of the feature extraction operator is shown in Fig.  1 .

The workflow of feature extraction operator.

In the first step, W high-quality solutions \({x}^{1},{x}^{2},{x}^{3},...,{x}^{W}\) are selected from the population. These W solutions will form the high-quality solution set S.

In the second step, calculate the mean \(\overline{x}\) and covariance matrix A of the high-quality solution set S:

Among them, \({x}^{i}={\left({x}_{1}^{i},{x}_{2}^{i},{x}_{3}^{i},...,{x}_{n}^{i}\right)}^{T}, i\in (1,...,W); {x}_{j}={\left({x}_{j}^{1},{x}_{j}^{2},{x}_{j}^{3},...,{x}_{j}^{W}\right)}^{T}, j\in (1,...,n)\)

In the third step, Cholesky decomposition is performed on the covariance matrix A. That is, the covariance matrix A is decomposed into the product of the lower triangular matrix and the transposition of the lower triangular matrix. Assuming that the lower triangular matrix is L, there is

Through formula \(A=L*{L}^{T}\) , we can calculate \({a}_{11}={l}_{11}^{2}\) , that is, \({l}_{11}=\sqrt{{a}_{11}}\) . Then, according to \({a}_{i1}={l}_{i1}*{l}_{11}\) , we can get \({l}_{i1}={a}_{i1}/{l}_{11}\) , so we can get the first column element of matrix L .

Assuming that we have calculated the first k-1 column elements of the matrix L. Through

In this way, we can solve the k-th column element of matrix L through the first k-1 column elements of matrix L. Then, we can solve matrix L by recursion.

In the fourth step, the sampling vector \(s={\left({s}_{1},...,{s}_{n}\right)}^{T}\) is generated by Gaussian distribution \(N\left(\text{0,0.7}\right)\) . Then, a feature solution is generated.

Among them, \({x}^{feature}={\left({x}_{1}^{feature},...,{x}_{n}^{feature}\right)}^{T}\)

It should be noted that the standard deviation std is an important parameter of the Gaussian distribution. In this paper, the standard deviation std is set to 0.7. The parameter analysis verifies that 0.7 is a reasonable standard deviation. For more details on parameter analysis, please browse the experiment chapter (Parameter sensitivity analysis section).

In the fifth step, assuming that the selected individual is \({x}^{i}({x}_{1}^{i},...,{x}_{n}^{i})\) . Based on binary crossover operator 29 and feature solution, the formula of generating offspring individual is as follows:

Among them, \({c({c}_{1},...,{c}_{n})}^{T}\) is the offspring individual. \({\beta }_{k}\) is dynamically determined by the feature factor \(\mu \) :

Among them, \(rand\) is used to generate a random number between 0 and 1; \(r and i(\text{0,1})\) is used to generate 0 or 1 randomly.

For the design principle of formula ( 15 ), please browse the Supplementary Information Document.

In the sixth step, the individual \(c\) is detected and repaired. When some components in individual \(c\) exceed the upper bound or lower bound, these components need to be repaired. The repair formula is as follows:

Among them, \({c}_{i}^{u}\) , \({c}_{i}^{l}\) represent the upper bound and lower bound of the i-th component of individual \(c\) , respectively. \({{c}{\prime}({c}_{1}{\prime},...,{c}_{n}{\prime})}^{T}\) represents the repaired individual.

Individual importance degree based on the Pareto front layer

When the number of optimization objectives is large, the algorithm will produce Pareto resistance, and the traditional Pareto dominance may fail. Some scholars try to modify the definition of traditional Pareto dominance by using dominance relaxation technology to enhance the selection pressure of the algorithm in terms of convergence. However, with the increase of the number of optimization objectives, the effect of dominance relaxation technology is getting worse and worse. They only focus on the modification of Pareto domination definition, but ignore the difference between objective values. Figure  2 shows 4 non-dominant individuals. Among them, individual B is the closest to Origin O, individual C is second, individual A is third, and individual D is the farthest from Origin O. This means that in the population, individual B is the most important, individual C is the second most important, individual A is the third most important, and individual D is the least important. In addition, we can also find from Fig.  2 that there is a significant difference between the objective values of individual B and the objective values of other individuals, that is, \(\sum_{X\in \left\{A,C,D\right\}}\sum_{i=1}^{2}{f}_{i}(B)-{f}_{i}(X)\) is the smallest. This shows that there is a special relationship between the importance of individuals and the difference of the objective values. Based on this discovery, if we can distinguish the importance of different individuals by using the difference between the objective values, we can further create the selection pressure of the algorithm in terms of convergence, and finally Pareto resistance will be eliminated. Therefore, based on Pareto front layer, we propose the concept of “individual importance degree”.

Schematic diagram of individual importance.

Assuming that there are n solutions in a certain Pareto front layer, the objective function values of these solutions are normalized to [0,1] based on the maximum and minimum values of each objective function. \({f}_{k}{\prime}({x}^{i})\) represents the k-th normalized objective function value of individual \({x}^{i}\) .

Define the Pareto dominance function

The trend of the Pareto dominance function is shown in Fig.  3 .

The trend of the Pareto dominance function.

Pareto dominance function can be used to reflect the dominance degree among different individuals. For example, \(PDF({f}_{k}{\prime}({x}^{i})-{f}_{k}{\prime}({x}^{j}))\) represents the dominance degree of individual \({x}^{i}\) to individual \({x}^{j}\) on the k-th objective function; \(PDF({f}_{k}{\prime}({x}^{j})-{f}_{k}{\prime}({x}^{i}))\) represents the dominance degree of individual \({x}^{j}\) to individual \({x}^{i}\) on the k-th objective function; Obviously, the greater the dominance degree, the better one individual is than another on one objective function. Therefore, on one objective function, the dominance degree of one individual to another can be expressed as:

On this basis, the dominance degree of one individual to another can be expressed as:

Further, the importance degree of one individual to another can be expressed as:

Importance degree can indicate the importance of one individual to another. The greater the importance degree, the more important one individual is than another.

Since a certain Pareto front layer has n solutions, each solution needs to be compared with other n-1 solutions, so as to construct n-1 competing pairs. Assuming that an individual is \({x}^{i}\) , then the n-1 competing pairs are \(\left({x}^{i},{x}^{1}\right),\left({x}^{i},{x}^{2}\right),...,\left({x}^{i},{x}^{j}\right),...,\left({x}^{i},{x}^{n}\right)\) , respectively (note: \(i\ne j\) ). Thus, the importance degree of individual \({x}^{i}\) to the other n-1 individuals is \(Imp\left({x}^{i},{x}^{1}\right),Imp\left({x}^{i},{x}^{2}\right),...,Imp\left({x}^{i},{x}^{j}\right),...,Imp\left({x}^{i},{x}^{n}\right)\) , respectively (note: \(i\ne j\) ).

Finally, the importance degree of the individual \({x}^{i}\) can be expressed as:

The importance degree of one individual can reflect the importance of the individual in the same Pareto front layer, so as to further distinguish the advantages and disadvantages of different individuals in the same front layer. The greater the importance degree of one individual, the more important it is in the same Pareto front layer.

Figure 4 shows the use of individual importance degree. Firstly, based on a certain Pareto front layer, the competition pools and competition pairs are constructed. Then, the individual importance degree of different individuals is calculated by Formula ( 24 ). Finally, the importance of different individuals in the same Pareto front layer is obtained.

The use of individual importance degree.

Taking the two-objective optimization problem as an example, it is assumed that there are 4 non-dominant individuals. They are \(A\left(\text{17,5}\right)\) , \(B\left(\text{9,7}\right)\) , \(C\left(\text{7,15}\right)\) and \(D\left(\text{5,25}\right)\) , respectively. It means that these 4 individuals belong to the first non-dominant layer, and their advantages and disadvantages can’t be compared by the non-dominant rank. The distribution of 4 individuals in the objective space is shown in Fig.  5 (a).

The distribution of 4 individuals.

In order to better compare the advantages and disadvantages of these 4 individuals, we use the individual importance degree to deal with these 4 individuals. Firstly, the objective function values of these 4 individuals are normalized to [0,1]. After normalization, the coordinates of these 4 individuals are \(A\left(\text{1,0}\right)\) , \(B\left(\text{0.333,0.1}\right)\) , \(C\left(\text{0.167,0.5}\right)\) and \(D\left(\text{0,1}\right)\) , respectively. The distribution of 4 individuals in the normalized objective space is shown in Fig.  5 (b). Next, according to Fig.  4 , the competition pools and competition pairs are constructed. Then, according to formula ( 22 ) and formula ( 23 ), the \(P\left({x}^{i},{x}^{j}\right)\) and \(Imp\left({x}^{i},{x}^{j}\right)\) of each competition pair are calculated. The calculation results are shown in Table 1 . Finally, according to the formula ( 24 ), the importance degree of these 4 individuals is 0.1918, 0.9488, 0.6673 and 0.1921, respectively. The results show that individual B is the most important, individual C is the second most important, individual D is the third most important, and individual A is the least important. This result is consistent with the intuitive perception that we get from Fig.  5 (b). Based on the above example, we believe that the concept of individual importance degree and related process are effective and can achieve the desired goals.

Repulsion field method

In the field of microphysics, when the distance between particles is too close, repulsion will push the particles away from their neighbors. On the contrary, when the distance between particles is too great, the repulsion will decrease and the particles tend to be close to the neighboring particles. This way makes the distribution of particles present a state of mutual coordination (As shown in Fig.  6 ). Based on the characteristics of particle distribution, a repulsion field method is proposed in this paper. The repulsion field is used to maintain the diversity of the population in the objective space, so that the population can be evenly distributed on the real PF.

The uniform distribution of microscopic particles.

Firstly, according to the maximum and minimum values of each objective function, the objective function values of all solutions in the population are normalized to [0,1]. \({f}_{k}{\prime}({x}^{i})\) represents the k-th normalized objective function value of individual \({x}^{i}\) .

Then, a repulsion potential field with repulsion radius r is constructed around each individual. Assuming that a repulsion potential field has been constructed for individual \({x}^{i}\) , then all individuals within the repulsion potential field will be subject to the repulsion potential from individual \({x}^{i}\) . The magnitude of the repulsion potential depends on the distance between other individuals and individual \({x}^{i}\) . When other individuals are outside the repulsion potential field of individual \({x}^{i}\) , the repulsion potential is 0. When other individuals are within the repulsion potential field of individual \({x}^{i}\) , the closer the other individuals are to individual \({x}^{i}\) , the greater the repulsion potential that they obtain. Assuming that there is individual \({x}^{j}\) , then the repulsion potential that individual \({x}^{j}\) obtains is

Among them, \(\rho \) is the gain coefficient of the repulsion potential field, usually set to 1; \(r\) is the radius of the repulsion potential field; \(dis\left({x}^{j},{x}^{i}\right)\) represents the euclidean distance between individual \({x}^{j}\) and individual \({x}^{i}\) in the objective space. The formula is as follows:

Further, the repulsion \(Rep\left({x}^{j},{x}^{i}\right)\) that individual \({x}^{j}\) obtains is the negative gradient of the repulsion potential \(Repfield\left({x}^{j},{x}^{i}\right)\) . The formula is as follows:

It means that when \(dis\left({x}^{j},{x}^{i}\right)\le r\) , the smaller \(dis\left({x}^{j},{x}^{i}\right)\) is, the larger \(Rep\left({x}^{j},{x}^{i}\right)\) is. when \(dis\left({x}^{j},{x}^{i}\right)>r\) , \(Rep\left({x}^{j},{x}^{i}\right)=0\) .

Based on the repulsion potential field, the total repulsion potential that individual \({x}^{j}\) obtains is

Finally, the total repulsion that individual \({x}^{j}\) obtains is

It should be noted that the repulsion potential and repulsion proposed in this paper are both vectors. It means that repulsion potential and repulsion have both magnitude and direction. The addition of different repulsion is the vector synthesis of repulsion, rather than the pure numerical addition. This is also an obvious feature that the repulsion field method is different from other scalar function methods (such as niche method). Figure 7 shows the vector synthesis process of repulsion in a two-dimensional space environment. Among them, F SUM is the total repulsion that individual A obtains; F BA is the repulsion generated by individual B to individual A; F CA is the repulsion generated by individual C to individual A.

The vector synthesis process of repulsion.

In the repulsion field method, the individual with large repulsion usually means that the individual is located in the multiple repulsion potential field that other individuals construct. It indicates that the individual is located in a dense area in the objective space and is close to other individuals. Therefore, individuals with large repulsion aren’t conducive to maintaining population diversity. Naturally, we hope that individuals with large repulsion can move away from dense areas in the objective space along the direction of repulsion. Based on this idea, firstly, we need to find some individuals closest to the direction of the repulsion to construct a high-quality solution set. Then, the feature extraction operator is used to extract the location features of the high-quality solution set. Finally, based on these features, individuals with large repulsion can evolve along the direction of repulsion. As shown in Fig.  8 , individual D and individual E are the individuals closest to the direction of repulsion. The feature extraction operator is used to extract the position features of these two individuals. Based on these features, individual A evolves into individual A*, which is far away from the previous dense area.

Individual A is far away from the dense area.

It should be noted that the feature extraction operator has the randomness caused by Gaussian sampling. Therefore, the evolution of individuals also has a certain degree of randomness.

Framework of MOEA/TS algorithm

The framework of the MOEA/TS algorithm is shown in Fig.  9 . Firstly, the relevant parameters of the algorithm are initialized; secondly, judge which state the algorithm is in. If the algorithm is in the convergence maintenance state, the following steps are adopted to improve the convergence of the algorithm: (1) Randomly select the parent individual. (2) Use feature extraction operator to generate offspring individuals. (3) If the offspring individual is superior to the individual with the worst convergence in the population, the worst individual is replaced by the offspring individual. If the algorithm is in the diversity maintenance state, the following steps are adopted to improve the diversity of the algorithm: (1) Select the individual with the worst diversity in the population. (2) Use feature extraction operator to generate offspring individuals. (3) If the offspring individual is superior to the individual with the worst diversity in the population, the worst individual is replaced by the offspring individual. If the algorithm is in the coordination state, the following steps are adopted to coordinate the convergence and diversity of the algorithm: (1) Randomly select the parent individual. (2) Use the Gaussian mutation operator to generate offspring individuals. (3) If the offspring individual is superior to the parent individual in convergence and diversity, the parent individual is replaced by the offspring individual. Then, it is judged whether the algorithm has completed the i-th iteration. If the algorithm doesn’t complete the i-th iteration, the corresponding maintenance step or coordination step is re-executed. If the algorithm completes the i-th iteration, the current state of the algorithm is updated. Finally, it is judged whether the algorithm ends. If the algorithm doesn’t end, the corresponding maintenance step or coordination step is performed according to the current state of the algorithm. If the algorithm is finished, the population is output.

The framework of MOEA/TS algorithm.

Description of MOEA/TS algorithm

Main framework.

This section describes the main framework of the MOEA/TS algorithm. The pseudo-code of the main framework of the MOEA/TS algorithm is shown in Algorithm 1. The main steps include: in line (1), initializing population P, repulsion field radius r, and state value (state=1 means that the algorithm is in convergence maintenance state, state=2 means that the algorithm is in diversity maintenance state, and state=3 means that the algorithm is in coordination state.); In line (2), the Front value, Imp value and Rep value of each solution in population P are calculated (The Front value is calculated by the fast non-dominated sorting method.); In line (3), it is judged whether the algorithm meets the termination condition (The termination condition is usually the maximum iterations.); In line (4), the count value is initialized. The count value is used to count the number of updated solutions in the i-th iteration; in lines (5)-(11), according to the current state of the algorithm, the update way of the population is selected. When state=1, the convergence of the population is updated. When state=2, the diversity of the population is updated. When state=3, the convergence and diversity of the population are coordinated; in line (12), the state value of the algorithm is updated according to the current state of the algorithm and the count value.

Main framework.

Convergence maintenance

This section mainly describes the convergence maintenance of the population. The pseudo-code of convergence maintenance is shown in Algorithm 2. The main steps include: in line (1), the algorithm enters the i-th iteration; In line (2), one parent individual is randomly selected from population P; In lines (3)–(4), based on Front, Imp, the high-quality solution set S is constructed; In line (5), the feature extraction operator is used to extract the features of the high-quality solution set S, and then assist the evolution of the parent individual; In line (6), the individual with the worst convergence in the population is found; In lines (7)–(13), if the offspring individual is superior to the individual with the worst convergence in the population, the worst individual is replaced by the offspring individual and flag is marked as 1. If the offspring individual is inferior to the individual with the worst convergence in the population, the flag is marked as 0. Among them, the flag value is used to indicate whether the population P has changed (flag=0 means that the population P hasn’t changed, flag=1 means that the population P has changed.); In lines (14)–(16), it is judged whether flag equals 1. If flag equals 1, the count value is updated, and the Front value, Imp value and Rep value of each solution in population P are updated.

Convergence maintenance.

Diversity maintenance

This section mainly describes the diversity maintenance of the population. The pseudo-code of diversity maintenance is shown in Algorithm 3. The main steps include: in line (1), the algorithm enters the i-th iteration; In line (2), the individual with the worst diversity in the population is found; In line (3), according to the direction of total repulsion that the worst individual obtains, the distance \({dis}_{j}\) can be calculated; In line (4), based on \({dis}_{j}\) , the high-quality solution set S is constructed; In line (5), the feature extraction operator is used to extract the features of the high-quality solution set S, and then assist the evolution of the worst individual; In lines (6)–(12), if the offspring individual is superior to the individual with the worst diversity in the population, the worst individual is replaced by the offspring individual and flag is marked as 1. If the offspring individual is inferior to the individual with the worst diversity in the population, the flag is marked as 0; In lines (13)–(15), it is judged whether flag equals 1. If flag equals 1, the count value is updated, and the Front value, Imp value and Rep value of each solution in population P are updated.

Diversity maintenance.

Coordination of convergence and diversity

This section mainly describes the coordination of convergence and diversity of the population. The pseudo-code of coordination of convergence and diversity is shown in Algorithm 4. The main steps include: in line (1), the algorithm enters the i-th iteration; In line (2), one parent individual is randomly selected from population P; In line (3), based on the parent individual, the Gaussian mutation operator is used to generate the offspring solution; In lines (4)–(10), if the offspring individual is superior to the parent individual in convergence and diversity, the parent individual is replaced by the offspring individual and flag is marked as 1. If the offspring individual is inferior to the parent individual in convergence and diversity, the flag is marked as 0; In lines (11)–(13), it is judged whether flag equals 1. If flag equals 1, the count value is updated, and the Front value, Imp value and Rep value of each solution in population P are updated.

Coordination.

This section mainly describes the feature extraction operator. The pseudo-code of the feature extraction operator is shown in Algorithm 5. The main steps include: in line (1), the features \(\overline{x},L\) of the high-quality solution set S are extracted by formula ( 4 ) and formula ( 13 ); In line (2), the sampling vector \(s={\left({s}_{1},...,{s}_{n}\right)}^{T}\) is generated by the Gaussian distribution \(N\left(\text{0,0.7}\right)\) ; In line (3), based on \(\text{s},\overline{x},L\) , the feature solution \({x}^{feature}\) is generated by formula ( 14 ); In line (4), based on parent, \({x}^{feature}\) , the offspring solution O is generated by formula ( 15 ).

Feature extraction.

Update of algorithm state

In this paper, the algorithm state is further updated according to the current state of the algorithm and the stability of the population. The pseudo-code of the update of the algorithm state is shown in Algorithm 6. When the algorithm is in the convergence maintenance state and the number of updated solutions in the i-th iteration is less than or equal to 5%*N, it is considered that the population tends to be stable in terms of convergence, then the algorithm turns to the diversity maintenance state; When the algorithm is in the diversity maintenance state and the number of updated solutions in the i-th iteration is less than or equal to 5%*N, it is considered that the population tends to be stable in terms of diversity, then the algorithm turns to the coordination state; When the algorithm is in the coordination state and the number of updated solutions in the i-th iteration is less than or equal to 5%*N, it is considered that the population tends to be stable in terms of coordination, then the algorithm turns to the convergence maintenance state. It should be noted that the threshold value T is a key parameter in measuring whether the population tends to be stable or not. In this paper, the threshold value T is set to 5%. The parameter analysis verifies that 5% is a reasonable threshold. For more details on parameter analysis, please browse the experiment chapter (Parameter sensitivity analysis section).

Determination state.

Computational complexity of one iteration of MOEA/TS algorithm

Assuming that the size of the population is N, the number of the objective function is m, the dimension of the decision variable is n, and the size of the high-quality solution set is W, then the computational complexity of Rep is O(mN2), the computational complexity of Front is O(mN2), and the computational complexity of Imp is O(mN2). The core steps of the feature extraction operator (Algorithm 5) include the construction of the covariance matrix and Cholesky decomposition. The computational complexity of covariance matrix construction is O(Wn2) and the computational complexity of Cholesky decomposition is O(n3/6). Therefore, the computational complexity of the feature extraction operator (Algorithm 5) is O(Wn2+n3/6). The core steps of convergence maintenance (Algorithm 2) include population ranking, feature extraction operator, selection of the worst individual, and updating of Front, Imp and Rep. Their computational complexity is O(N2), O(Wn2+n3/6), O(N), O(mN2), O(mN2), O(mN2), respectively. Therefore, the computational complexity of convergence maintenance (Algorithm 2) is O(N(N2+Wn2+n3/6+N+3mN2)). The core steps of diversity maintenance (Algorithm 3) include selection of the worst individual, distance calculation, population ranking, feature extraction operator, and updating of Front, Imp and Rep. Their computational complexity is O(N), O(nN), O(N2), O(Wn2+n3/6), O(mN2), O(mN2), O(mN2), respectively. Therefore, the computational complexity of diversity maintenance (Algorithm 3) is O(N(N+nN+N2+Wn2+n3/6+3mN2)). The core steps of coordination of convergence and diversity (Algorithm 4) include the Gaussian mutation operator, and updating of Front, Imp and Rep. Their computational complexity is O(n), O(mN2), O(mN2), O(mN2), respectively. Therefore, the computational complexity of coordination of convergence and diversity (Algorithm 4) is O(N(n+3mN2)). The computational complexity of Determination State (Algorithm 6) is O(1). Based on the above computational complexity analysis, the computational complexity of one iteration of the MOEA/TS algorithm is max{O(N(N2+Wn2+n3/6+N+3mN2)), O(N(N+nN+N2+Wn2+n3/6+3mN2)), O(N(n+3mN2))}+O(1)≈max{O(NWn2+Nn3+mN3), O(NWn2+Nn3+mN3), O(mN3)}= O(NWn2+Nn3+mN3). In this paper, N>>max{W, n, m}. Therefore, the computational complexity of the MOEA/TS algorithm is O(mN3). As a reference algorithm, the computational complexity of the NSGA-III algorithm is O(mN2). The computational complexity of the MOEA/TS algorithm is an order of magnitude higher than that of the NSGA-III algorithm. This shows that the MOEA/TS algorithm is an expensive many-objective optimization algorithm.

It should be noted that although MOEA/TS algorithm has a higher computational complexity. But compared with the NSGA-III algorithm, the MOEA/TS algorithm also has greater advantages. In terms of convergence optimization, the NSGA-III algorithm adopts the traditional definition of Pareto domination. Obviously, the traditional definition can’t solve the problem of Pareto resistance. MOEA/TS algorithm uses the concept of “individual importance degree”. Individual importance degree can solve the problem of Pareto resistance. In terms of diversity optimization, the NSGA-III algorithm uses predefined reference points. The predefined reference points can’t solve the problem that the population can't be evenly distributed on the real PF in the high-dimensional objective space. MOEA/TS algorithm uses the repulsion field method. The repulsion field method can solve the problem that the population can’t be evenly distributed on the real PF in the high-dimensional objective space. In terms of algorithm architecture, the NSGA-III algorithm adopts the serial algorithm architecture. The serial algorithm architecture is difficult to coordinate the convergence optimization and diversity optimization of the algorithm. MOEA/TS algorithm adopts the concurrent algorithm architecture. The concurrent algorithm architecture can coordinate the convergence optimization and diversity optimization of the algorithm. In terms of operators, the NSGA-III algorithm uses the traditional binary crossover operator and polynomial mutation operator. The evolutionary ability of these two operators is weak. MOEA/TS algorithm uses feature extraction operator. Feature extraction operator has strong evolutionary ability. Therefore, the MOEA/TS algorithm has better performance than the NSGA-III algorithm. The comparison results support our conclusion. For the comparison results of these two algorithms, please browse Supplementary Information Document.

Experimental results and analysis

Experimental settings, configuration of experimental software and hardware.

The hardware and software configurations of the experiment are shown in Table 2 . Among them, PlatEMO 30 is a professional many-objective optimization experiment platform. The platform includes multiple test function sets and many-objective optimization algorithms.

Test function

The test functions used in the experiment include: DTLZ test function set (DTLZ1-7), MAF test function set (MAF1-6) and WFG test function set (WFG1-9). Literature 31 describes the characteristics of related test functions. The parameter settings of the related test functions are shown in Table 3 .

Comparison algorithm

In order to verify the performance of MOEA/TS algorithm in the many-objective optimization field, this paper compares MOEA/TS algorithm with 7 advanced many-objective optimization algorithms. These 7 many-objective optimization algorithms include: VMEF 32 , BiGE-BEW 33 , MOEA/DG 34 , MOEA/D 35 , LSMaODE 36 , MaOEA/IT 23 and MaOEA/IGD 37 .

For all test cases, Wilcoxon rank sum test at 5% significance level 38 is used to compare the significance of the difference between the MOEA/TS algorithm and the comparison algorithms. The symbol “+” indicates that the comparison algorithms are significantly better than the MOEA/TS algorithm; the symbol “-“indicates that the comparison algorithms are significantly inferior to the MOEA/TS algorithm. The symbol “=” indicates that there is no significant difference between the MOEA/TS algorithm and the comparison algorithms.

Performance evaluation

In the aspect of performance evaluation, this paper uses inverted generational distance plus (IGD+) and hypervolume (HV) 39 to measure the performance of many-objective optimization algorithm. The smaller the IGD+ value that the algorithm obtains, the better the performance of the algorithm. The larger the HV value that the algorithm obtains, the better the performance of the algorithm.

In order to facilitate observation, we provide the normalized HV value of each algorithm relative to the best HV result. This normalization makes all the results lie in the range [0,1], and 1 represents the best value.

Considering the length of the paper, we only show the IGD+ values of different algorithms in the experiment chapter. For the HV values of different algorithms, please browse the Supplementary Information Document.

Parameter setting

In terms of algorithm parameters, according to some existing parameter research results 13 , 40 , the feature factor \(\mu \) is set to 20 in this paper. According to the parameter sensitivity analysis, the number of high-quality solutions W is set to 9 in this paper. The parameter sensitivity analysis of W is detailed in the subsequent chapters.

The algorithm parameters of the 7 comparison algorithms are determined according to the best parameters provided by the corresponding literature.

Performance comparison under benchmark test functions

Performance comparison under dtlz test function set.

In this paper, each algorithm is executed 30 times to get the average data as shown in Table 4 . As can be seen from Table 4 , MOEA/TS algorithm wins the first place in 15 test cases; BiGE-BEW algorithm wins the first place in 5 test cases; MOEA/D algorithm wins the first place in 15 test cases. In the 35 test cases, the number of MOEA/TS algorithm is significantly superior to VMEF algorithm, BiGE-BEW algorithm, MOEA/DG algorithm, MOEA/D algorithm, LSMaODE algorithm, MaOEA/IT algorithm and MaOEA/IGD algorithm is 21, 27, 25, 16, 32, 35 and 31, respectively. The number of MOEA/TS algorithm is significantly inferior to VMEF algorithm, BiGE-BEW algorithm, MOEA/DG algorithm, MOEA/D algorithm, LSMaODE algorithm, MaOEA/IT algorithm and MaOEA/IGD algorithm is 6, 5, 5, 15, 1, 0 and 0, respectively. Statistically, the number of MOEA/TS algorithm is similar to VMEF algorithm, BiGE-BEW algorithm, MOEA/DG algorithm, MOEA/D algorithm, LSMaODE algorithm, MaOEA/IT algorithm and MaOEA/IGD algorithm is 8, 3, 5, 4, 2, 0 and 4, respectively. Therefore, in the DTLZ test function set, MOEA/TS algorithm and MOEA/D algorithm have the best performance. The performance of VMEF algorithm, MOEA/DG algorithm, BiGE-BEW algorithm and LSMaODE algorithm decreases in turn. The performance of MaOEA/IGD algorithm and MaOEA/IT algorithm is similar and the worst.

Based on Table 4 , we further analyze the performance of these algorithms. In the DTLZ test function set, MOEA/TS algorithm performs poorly on DTLZ1, DTLZ5 and DTLZ6 test functions. The possible reasons are that the DTLZ1 test function has multiple local optima, and the DTLZ5 and DTLZ6 test functions have a narrow convergence curve. In the DTLZ1 test function, although the repulsion field method of the MOEA/TS algorithm makes the population widely distributed. However, its population distribution isn’t uniform and regular. The population distribution of some algorithms using predefined weight vectors is uniform and regular. In the DTLZ5 and DTLZ6 test functions, the coordination mechanism of MOEA/TS algorithm fails. The narrow convergence curve makes the population more concentrated, but the repulsion field method will disperse the population. The coordination mechanism is difficult to play a role.

The real Pareto front of DTLZ test function set is regular and the function complexity isn’t high. Therefore, algorithms with better diversity may be more popular. MOEA/D algorithm uses predefined weight vectors to maintain diversity and aggregation functions to maintain convergence. Therefore, it has good performance. VMEF algorithm uses different convergence ranking methods to deal with different test problems. Therefore, VMEF algorithm is good in convergence and poor in diversity. Based on the convergence measure and diversity measure, BiGE-BEW algorithm transforms the many-objective optimization problem into a two-objective optimization problem. In theory, the algorithm should perform well. However, there are defects in its convergence and diversity measurement formula. Finally, the experimental results of the algorithm aren’t as good as the expected results. MOEA/DG algorithm still uses the traditional dominance relationship to maintain the convergence of external archives. Therefore, MOEA/DG algorithm is poor in convergence and good in diversity. LSMaODE algorithm divides the population into two subpopulations and uses different strategies to optimize them. Because the real Pareto front of DTLZ test function set isn’t complex, the advantage of this multi-population algorithm architecture isn’t obvious. Therefore, compared with other algorithms, its performance is mediocre. MaOEA/IT algorithm optimizes convergence and diversity through two independent phases. However, the algorithm's performance is always poor because it doesn’t alleviate the contradiction between convergence and diversity. The reference Pareto front of MaOEA/IGD algorithm is poor. Therefore, the algorithm’s performance is always poor.

Performance comparison under MAF test function set

In this paper, each algorithm is executed 30 times to get the average data as shown in Table 5 . As can be seen from Table 5 , MOEA/TS algorithm wins the first place in 10 test cases; BiGE-BEW algorithm wins the first place in 8 test cases; MOEA/DG algorithm wins the first place in 2 test cases; MOEA/D algorithm wins the first place in 5 test cases; LSMaODE algorithm wins the first place in 5 test cases. In the 30 test cases, the number of MOEA/TS algorithm is significantly superior to VMEF algorithm, BiGE-BEW algorithm, MOEA/DG algorithm, MOEA/D algorithm, LSMaODE algorithm, MaOEA/IT algorithm and MaOEA/IGD algorithm is 22, 18, 25, 21, 20, 27 and 30, respectively. The number of MOEA/TS algorithm is significantly inferior to VMEF algorithm, BiGE-BEW algorithm, MOEA/DG algorithm, MOEA/D algorithm, LSMaODE algorithm, MaOEA/IT algorithm and MaOEA/IGD algorithm is 6, 11, 2, 5, 9, 1 and 0, respectively. Statistically, the number of MOEA/TS algorithm is similar to VMEF algorithm, BiGE-BEW algorithm, MOEA/DG algorithm, MOEA/D algorithm, LSMaODE algorithm, MaOEA/IT algorithm and MaOEA/IGD algorithm is 2, 1, 3, 4, 1, 2 and 0, respectively. Therefore, in the MAF test function set, MOEA/TS algorithm has the best performance. The performance of BiGE-BEW algorithm, LSMaODE algorithm, VMEF algorithm, MOEA/D algorithm, MOEA/DG algorithm and MaOEA/IT algorithm decreases in turn. The performance of MaOEA/IGD algorithm is the worst.

Based on Table 5 , we further analyze the performance of these algorithms. In the MAF test function set, MOEA/TS algorithm performs poorly on MAF2 and MAF3 test functions. The possible reasons are that the MAF2 test function greatly increases the difficulty of convergence on the basis of the DTLZ2 test function, and the MAF3 test function has a convex Pareto front and many local fronts. In the MAF2 test function, although the MOEA/TS algorithm can recognize the advantage and disadvantage of different individuals in the same front layer, the evolutionary efficiency of the MOEA/TS algorithm isn’t ideal. In other words, after the algorithm is finished, the population still has the large evolution potential in convergence. In the MAF3 test function, MOEA/TS algorithm can effectively deal with the convex Pareto front. However, MOEA/TS algorithm is difficult to deal with multiple local fronts because feature extraction operator of MOEA/TS algorithm is difficult to extract features of multiple local fronts.

MAF test function set is the variety of DTLZ test function set. It adds a lot of characteristics to the DTLZ test function set. For example, degenerate, convex, concave, partial, multimodal, deceptive, et al. Therefore, the MAF test function set is more difficult in terms of convergence and diversity. Based on the convergence measure and diversity measure, BiGE-BEW algorithm transforms the many-objective optimization problem into a two-objective optimization problem. Although there are some defects in its diversity and convergence measurement formula, BiGE-BEW algorithm shows good performance in convergence when dealing with more complex MaOPs. VMEF algorithm uses different convergence ranking methods to deal with different test problems. However, the complex Pareto fronts and diversified characteristics still pose a great challenge to VMEF algorithm. Therefore, the performance of VMEF algorithm is mediocre. MOEA/DG algorithm still uses the traditional dominance relationship to maintain the convergence of external archives. Therefore, MOEA/DG algorithm is poor in convergence. MOEA/D algorithm uses predefined weight vectors to maintain diversity and aggregation functions to maintain convergence. MOEA/D algorithm can easily deal with the DTLZ test function set. However, its performance isn’t ideal when dealing with more complex MAF test function set. Surprisingly, LSMaODE algorithm shows good performance. We speculate that the possible reason is that the real Pareto front of the MAF test function set is complex, and then the advantages of multi-population algorithm architecture can be reflected. MaOEA/IT algorithm optimizes convergence and diversity through two independent phases. However, the algorithm’s performance is always poor because it doesn’t alleviate the contradiction between convergence and diversity. The reference Pareto front of MaOEA/IGD algorithm is poor. Therefore, the algorithm’s performance is always poor.

Performance comparison under WFG test function set

In this paper, each algorithm is executed 30 times to get the average data as shown in Table 6 . As can be seen from Table 6 , MOEA/TS algorithm wins the first place in 27 test cases; VMEF algorithm wins the first place in 8 test cases; BiGE-BEW algorithm wins the first place in 6 test cases; MOEA/DG algorithm wins the first place in 1 test case; LSMaODE algorithm wins the first place in 3 test cases. In the 45 test cases, the number of MOEA/TS algorithm is significantly superior to VMEF algorithm, BiGE-BEW algorithm, MOEA/DG algorithm, MOEA/D algorithm, LSMaODE algorithm, MaOEA/IT algorithm and MaOEA/IGD algorithm is 26, 29, 42, 45, 39, 45 and 43, respectively. The number of MOEA/TS algorithm is significantly inferior to VMEF algorithm, BiGE-BEW algorithm, MOEA/DG algorithm, MOEA/D algorithm, LSMaODE algorithm, MaOEA/IT algorithm and MaOEA/IGD algorithm is 10, 9, 3, 0, 3, 0 and 0, respectively. Statistically, the number of MOEA/TS algorithm is similar to VMEF algorithm, BiGE-BEW algorithm, MOEA/DG algorithm, MOEA/D algorithm, LSMaODE algorithm, MaOEA/IT algorithm and MaOEA/IGD algorithm is 9, 7, 0, 0, 3, 0 and 2, respectively. Therefore, in the WFG test function set, MOEA/TS algorithm has the best performance. The performance of VMEF algorithm, BiGE-BEW algorithm, LSMaODE algorithm and MOEA/DG algorithm decreases in turn. The performance of MaOEA/IGD algorithm, MOEA/D algorithm and MaOEA/IT algorithm is similar and the worst.

Based on Table 6 , we further analyze the performance of these algorithms. MOEA/TS algorithm performs well in all WFG test functions. The possible reason is that the problem characteristics of the WFG test function set are bias, fraud and degradation. The WFG test function set is more difficult than the DTLZ test function set. However, the problem characteristics of the WFG test function set don’t include multiple local fronts (From the previous analysis, we know that MOEA/TS algorithm isn’t good at dealing with multiple local fronts.). MOEA/TS algorithm can deal with these problem characteristics. Therefore, MOEA/TS algorithm performs well in all WFG test functions. It should be noted that the WFG3 test function has a narrow convergence curve, but the performance of MOEA/TS algorithm is still the best. This is an interesting phenomenon. Because from the previous analysis, we know that MOEA/TS algorithm isn’t good at dealing with test functions with narrow convergence curves (such as DTLZ5 and DTLZ6 test functions). Based on the convergence difficulty of the WFG test function set, we speculate that the performance of the other 7 algorithms is worse, thus highlighting the performance of MOEA/TS algorithm.

Compared with the DTLZ test function set, the MAF test function set is more difficult in terms of convergence and diversity. VMEF algorithm uses different convergence ranking methods to deal with different test problems. This approach helps VMEF algorithm to deal with different problem characteristics. Therefore, the performance of VMEF algorithm is good. Based on the convergence measure and diversity measure, BiGE-BEW algorithm transforms the many-objective optimization problem into a two-objective optimization problem. Although there are some defects in its diversity and convergence measurement formula, BiGE-BEW algorithm shows good performance in convergence when dealing with more complex MaOPs. MOEA/DG algorithm still uses the traditional dominance relationship to maintain the convergence of external archives. Therefore, MOEA/DG algorithm is poor in convergence. MOEA/D algorithm uses predefined weight vectors to maintain diversity and aggregation functions to maintain convergence. This approach isn’t suitable for dealing with test functions with bias characteristic. Therefore, the performance of MOEA/D algorithm is the worst. LSMaODE algorithm divides the population into two subpopulations and uses different strategies to optimize them. Because most WFG test functions have bias characteristic, LSMaODE algorithm doesn’t consider the bias problem. Therefore, the performance of LSMaODE algorithm is mediocre. MaOEA/IT algorithm optimizes convergence and diversity through two independent phases. However, the algorithm’s performance is always poor because it doesn’t alleviate the contradiction between convergence and diversity. The reference Pareto front of MaOEA/IGD algorithm is poor. Therefore, the algorithm’s performance is always poor.

Comparison and analysis

By synthesizing Tables 4 , 5 , 6 , we can obtain the data shown in Table 7 . As can be seen from Tables 4 , 5 , 6 , MOEA/TS algorithm wins the first place in 52 test cases; VMEF algorithm wins the first place in 8 test cases; BiGE-BEW algorithm wins the first place in 19 test cases; MOEA/DG algorithm wins the first place in 3 test cases; MOEA/D algorithm wins the first place in 20 test cases; LSMaODE algorithm wins first place in 8 test cases. As can be seen from Table 7 , in the 110 test cases, the number of MOEA/TS algorithm is significantly superior to VMEF algorithm, BiGE-BEW algorithm, MOEA/DG algorithm, MOEA/D algorithm, LSMaODE algorithm, MaOEA/IT algorithm and MaOEA/IGD algorithm is 69, 74, 92, 82, 91, 107 and 104, respectively. The number of MOEA/TS algorithm is significantly inferior to VMEF algorithm, BiGE-BEW algorithm, MOEA/DG algorithm, MOEA/D algorithm, LSMaODE algorithm, MaOEA/IT algorithm and MaOEA/IGD algorithm is 22, 25, 10, 20, 13, 1 and 0, respectively. Statistically, the number of MOEA/TS algorithm is similar to VMEF algorithm, BiGE-BEW algorithm, MOEA/DG algorithm, MOEA/D algorithm, LSMaODE algorithm, MaOEA/IT algorithm and MaOEA/IGD algorithm is 19, 11, 8, 8, 6, 2 and 6, respectively. Based on the above data, we can get the following conclusions: MOEA/TS algorithm has the best performance; the performance of BiGE-BEW algorithm, VMEF algorithm, MOEA/D algorithm, LSMaODE algorithm, MOEA/DG algorithm and MaOEA/IT algorithm decreases in turn. MaOEA/IGD algorithm has the worst performance.

In addition to the above conclusions, we can also observe 3 interesting phenomena:

(1) In the MAF test function set and WFG test function set, MOEA/TS algorithm has no competitors. However, in the DTLZ test function set, MOEA/TS algorithm and MOEA/D algorithm are competitors, and they have similar performance. This is because most DTLZ test functions have regular PF, while most MAF test functions and WFG test functions have more complex PF. It can be seen from Sect. " Introduction " that MOEA/D algorithm is suitable for MaOPs with regular PF. Therefore, in the DTLZ test function set, MOEA/D algorithm can compete with MOEA/TS algorithm. In the MAF test functions and WFG test functions, only MOEA/TS algorithm shows excellent performance.

(2) The performance of MOEA/TS algorithm is better on the test cases with 10 objectives, 15 objectives and 20 objectives. The performance of MOEA/TS algorithm is relatively ordinary on the test cases with 5 objectives and 8 objectives. This is because when the number of optimization objectives is small, most many-objective optimization algorithms perform well. Compared with other many-objective optimization algorithms, the advantages of MOEA/TS algorithm aren’t obvious. However, with the increase of the number of optimization objectives, the performance of other many-objective optimization algorithms becomes worse and worse. In contrast, the performance of MOEA/TS algorithm isn’t significantly affected. Therefore, compared with other many-objective optimization algorithms, MOEA/TS algorithm has obvious advantages. This shows that MOEA/TS algorithm is more suitable for solving MaOPs with more than 10 objectives.

(3) Without considering MOEA/TS algorithm, MOEA/D algorithm has the best performance in the DTLZ test function set. BiGE-BEW algorithm has the best performance in the MAF test function set. VMEF algorithm has the best performance in the WFG test function set. This shows that different many-objective optimization algorithms are suitable for different test function sets. However, MOEA/TS algorithm can show excellent performance on three test function sets. This indicates that MOEA/TS algorithm has strong universality and applicability.

Distribution diagram of solutions in the objective space

In order to describe the distribution of solutions in the high-dimensional objective space more intuitively, this paper draws the distribution diagram of solutions in the objective space. Considering the length of the paper, it is unrealistic to show the distribution diagrams of all test functions. Therefore, this section only shows the distribution diagrams of 3 representative test cases. These 3 test cases are DTLZ2 test case with 20 objectives, MAF1 test case with 15 objectives and WFG3 test case with 10 objectives, respectively.

Figure 10 shows the distribution diagrams of each algorithm on DTLZ2 test case with 20 objectives. It can be seen from Fig.  10 that distribution diagrams of MOEA/TS algorithm, BiGE-BEW algorithm, MOEA/DG algorithm and MOEA/D algorithm are similar, which indicates that these 4 algorithms are excellent in convergence and diversity; VMEF algorithm and LSMaODE algorithm are good in diversity, but poor in convergence; MaOEA/IT algorithm and MaOEA/IGD algorithm are very poor in convergence and diversity.

Distribution diagrams of each algorithm on DTLZ2 test case with 20 objectives.

Figure 11 shows the distribution diagrams of each algorithm on MAF1 test case with 15 objectives. It can be seen from Fig.  11 that MOEA/TS algorithm and VMEF algorithm are good in convergence, but poor in diversity; BiGE-BEW algorithm and LSMaODE algorithm are good in diversity, but poor in convergence. MOEA/DG algorithm, MOEA/D algorithm, MaOEA/IT algorithm and MaOEA/IGD algorithm are very bad in convergence and diversity.

Distribution diagrams of each algorithm on MAF1 test case with 15 objectives.

Figure 12 shows the distribution diagrams of each algorithm on WFG3 test case with 10 objectives. It can be seen from Fig.  12 that MOEA/TS algorithm has the best convergence and diversity; LSMaODE algorithm is also excellent, only slightly worse than MOEA/TS algorithm in terms of diversity; BiGE-BEW algorithm and MOEA/DG algorithm are good in diversity, but poor in convergence. VMEF algorithm is good in convergence, but poor in diversity. MOEA/D algorithm, MaOEA/IT algorithm and MaOEA/IGD algorithm are very bad in convergence and diversity.

Distribution diagrams of each algorithm on WFG3 test case with 10 objectives.

Evolution curve analysis of the algorithm

This section takes DTLZ2 test case with 20 objectives, MAF1 test case with 15 objectives and WFG3 test case with 10 objectives as examples to display the evolution curves of 8 algorithms (as shown in Figs.  13 , 14 , 15 ).

Evolution curve of each algorithm on DTLZ2 test case with 20 objectives.

In Figure 13 , in terms of the final IGD+ value of the algorithm, MOEA/TS algorithm has the smallest IGD+ value, while the IGD+ values of MOEA/DG algorithm, BiGE-BEW algorithm, MOEA/D algorithm, LSMaODE algorithm, VMEF algorithm and MaOEA/IGD algorithm successively increase, and MaOEA/IT algorithm has the largest IGD+ value. This shows that MOEA/TS algorithm has the best convergence and diversity within the specified number of iterations. In terms of the evolution of the algorithm, the final IGD+ values of all algorithms are smaller than the initial IGD+ values. This shows that all algorithms have strong evolution ability, especially MOEA/TS algorithm has the strongest evolution ability. In terms of algorithm fluctuation, MaOEA/IT algorithm fluctuates greatly. This shows that MaOEA/IT algorithm isn’t stable. Based on the above analysis, we believe that MOEA/TS algorithm has the best comprehensive performance on DTLZ2 test case with 20 objectives, and is suitable for solving DTLZ2 test problem with 20 objectives.

In Figure 14 , in terms of the final IGD+ value of the algorithm, MOEA/TS algorithm has the smallest IGD+ value, while the IGD+ values of BiGE-BEW algorithm, VMEF algorithm, LSMaODE algorithm, MaOEA/IGD algorithm, MOEA/D algorithm and MOEA/DG algorithm successively increase, and MaOEA/IT algorithm has the largest IGD+ value. This shows that MOEA/TS algorithm has the best convergence and diversity within the specified number of iterations. In terms of the evolution of the algorithm, the final IGD+ values of all algorithms are smaller than the initial IGD+ values. This shows that all algorithms have strong evolution ability, especially MOEA/TS algorithm has the strongest evolution ability. In terms of algorithm fluctuation, MaOEA/IT algorithm fluctuates greatly. This shows that MaOEA/IT algorithm isn’t stable. Based on the above analysis, we believe that MOEA/TS algorithm has the best comprehensive performance on MAF1 test case with 15 objectives, and is suitable for solving MAF1 test problem with 15 objectives.

Evolution curve of each algorithm on MAF1 test case with 15 objectives.

In Fig.  15 , in terms of the final IGD+ value of the algorithm, MOEA/TS algorithm has the smallest IGD+ value, while the IGD+ values of LSMaODE algorithm, MOEA/DG algorithm, VMEF algorithm, BiGE-BEW algorithm, MaOEA/IGD algorithm and MOEA/D algorithm successively increase, and MaOEA/IT algorithm has the largest IGD+ value. This shows that MOEA/TS algorithm has the best convergence and diversity within the specified number of iterations. In terms of the evolution of the algorithm, the final IGD+ values of the MaOEA/IT algorithm, VMEF algorithm, MaOEA/IGD algorithm, BiGE-BEW algorithm and VMEF algorithm are all greater than the initial IGD+ values. This shows that the performance of these 5 algorithms deteriorates during evolution, and they aren’t suitable for dealing with WFG3 test problem with 10 objectives. The initial IGD+ value of MOEA/DG algorithm is close to the final IGD+ value, and the IGD+ value of MOEA/DG algorithm fluctuates little during the evolution. This shows that MOEA/DG algorithm is insensitive to evolution. Only the final IGD+ values of LSMaODE algorithm and MOEA/TS algorithm are less than the initial IGD+ values. This shows that LSMaODE algorithm and MOEA/TS algorithm have strong evolution ability, especially MOEA/TS algorithm has the strongest evolution ability. In terms of algorithm fluctuation, MOEA/D algorithm, MaOEA/IT algorithm and MaOEA/IGD algorithm have greater fluctuation. This shows that these 3 algorithms aren’t stable. Based on the above analysis, we believe that MOEA/TS algorithm has the best comprehensive performance on WFG3 test case with 10 objectives, and is suitable for solving WFG3 test problem with 10 objectives.

Evolution curve of each algorithm on WFG3 test case with 10 objectives.

In addition, we can also observe an interesting phenomenon from Fig.  13 to Fig.  15 : the IGD+ values of some algorithms sometimes increase significantly with the increase of iterations. That is, the performance of some algorithms sometimes deteriorates seriously with the increase of iterations. The reasons for this phenomenon may include three aspects: (1) The algorithm doesn’t adopt the elite preservation strategy. Some high-quality solutions may gradually disappear; (2) Due to the complexity of the optimization problems, the evolutionary direction of the population may be misled by some pseudo-elite individuals; (3) The convergence optimization and diversity optimization of the algorithm aren’t coordinated. The optimization of convergence may affect the optimization of diversity or the optimization of diversity may affect the optimization of convergence. It can be seen from the pseudo-code of the algorithm in Section 3.5 that the MOEA/TS algorithm proposed in this paper considers the above three aspects. Therefore, MOEA/TS algorithm can effectively alleviate this phenomenon.

Effectiveness verification of innovation part

In order to verify the effectiveness of the innovative parts, 4 variants are designed in this section. As follows:

MOEA/TS-1 algorithm: The feature extraction operator in MOEA/TS algorithm is changed to the binary crossover operator and polynomial mutation operator;

MOEA/TS-2 algorithm: The repulsion field method in MOEA/TS algorithm is removed;

MOEA/TS-3 algorithm: The concurrent architecture in MOEA/TS algorithm is changed to serial architecture;

MOEA/TS-4 algorithm: The individual importance degree in MOEA/TS algorithm is removed.

This paper takes WFG test function set (45 test cases) as samples, and then verifies the performance of 5 algorithms. In this paper, 5 algorithms are executed 30 times to get the average data as shown in Table 8 . As can be seen from Table 8 , MOEA/TS algorithm wins the first place in 24 test cases; MOEA/TS-1 algorithm wins the first place in 13 test cases; MOEA/TS-2 algorithm wins the first place in 7 test cases; MOEA/TS-3 algorithm wins the first place in 1 test case. In the 45 test cases, the number of MOEA/TS algorithm is significantly superior to MOEA/TS-1 algorithm, MOEA/TS-2 algorithm, MOEA/TS-3 algorithm and MOEA/TS-4 algorithm is 21, 30, 40 and 45, respectively. The number of MOEA/TS algorithm is significantly inferior to MOEA/TS-1 algorithm, MOEA/TS-2 algorithm, MOEA/TS-3 algorithm and MOEA/TS-4 algorithm is 11, 6, 0 and 0, respectively. Statistically, the number of MOEA/TS algorithm is similar to MOEA/TS-1 algorithm, MOEA/TS-2 algorithm, MOEA/TS-3 algorithm and MOEA/TS-4 algorithm is 13, 9, 5 and 0, respectively. The average ranking of MOEA/TS algorithm is about 1.64; the average ranking of MOEA/TS-1 algorithm is about 2.02; the average ranking of MOEA/TS-2 algorithm is about 2.62; the average ranking of MOEA/TS-3 algorithm is about 3.71; the average ranking of MOEA/TS-4 algorithm is 5.

Therefore, we think that the 4 innovative parts of MOEA/TS algorithm are necessary and indispensable. The lack of any innovative parts will seriously affect the performance of MOEA/TS algorithm. This shows that our innovations are effective. In addition, based on the above data, we can also find that “individual importance degree” has the greatest influence on the algorithm; the algorithm architecture ranks second; the repulsion field method ranks third; the feature extraction operator ranks fourth.

Ablation experiment of selection approach

In the feature extraction operator, we select W high-quality solutions. To prove the effectiveness of this selection approach over random selection, the ablation experiment will be performed in this sect. " Introduction " variant is designed in this section. As follows:

MOEA/TS-5 algorithm: W solutions are randomly selected in the feature extraction operator.

This paper takes WFG test function set (45 test cases) as samples, and then verifies the performance of 2 algorithms. In this paper, 2 algorithms are executed 30 times to get the average data as shown in Table 9 . As can be seen from Table 9 , MOEA/TS algorithm wins the first place in 45 test cases. In the 45 test cases, the number of MOEA/TS algorithm is significantly superior to MOEA/TS-5 algorithm is 42. The number of MOEA/TS algorithm is significantly inferior to MOEA/TS-5 algorithm is 0. Statistically, the number of MOEA/TS algorithm is similar to MOEA/TS-5 algorithm is 3. Therefore, we believe that the performance of MOEA/TS algorithm is better than MOEA/TS-5 algorithm in the WFG test function set. It proves that the selection approach that we use is better than random selection in the feature extraction operator.

In addition, the performance of MOEA/TS-5 algorithm isn’t as good as that of MOEA/TS-1 algorithm. It means that the performance of the feature extraction operator based on random selection is even worse than that of some classical operators. The possible reason is that the randomly selected solution set will cause the feature extraction operator to extract many bad features. These bad features hinder individual evolution, which makes the convergence maintenance state and diversity maintenance state of MOEA/TS algorithm fail for a long time, and only the coordination state can play some role. The architecture of the MOEA/TS algorithm is undermined by some bad features.

Parameter sensitivity analysis.

The algorithm parameters analyzed in this paper are mainly the number of high-quality solutions W, threshold value T, standard deviation std. Due to the high complexity of the WFG3 test case with 10 objectives, it is difficult for the population of each algorithm to cover the real Pareto front, so this paper considers the WFG3 test case with 10 objectives as the main function of parameter analysis.

The initial value and value range of each parameter are shown in Table 10 .

As shown in Fig.  16 , when \(W<9\) , the IGD + value of the algorithm decreases significantly with the increase of W . It means that when \(W<9\) , the performance of the feature extraction operator is greatly improved with the increase of W . This is because the features extracted by the feature extraction operator are closer to the ideal situation. When \(W=9\) , the IGD + value of the algorithm is minimum. This shows that when \(W=9\) , the feature extraction operator performs best. When \(W>9\) , the IGD + value of the algorithm increases slowly. It means that when \(W>9\) , the performance of the feature extraction operator deteriorates gradually with the increase of W . This is because some features are over-extracted by feature extraction operators. Therefore, for WFG3 test case with 10 objectives, \(W=9\) is the best parameter selection.

The corresponding relationship between IGD + value and W.

As shown in Fig.  17 , when \(T<5\%\) , the IGD + value of the algorithm decreases significantly with the increase of T . This is because if the threshold value T is too small, the algorithm will remain in the same state for a long time, and it is difficult to be adjusted to other states. Convergence and diversity of algorithm will also be difficult to balance. This situation will be improved with the increase of T . When \(T=5\%\) , the IGD + value of the algorithm is minimum. This shows that when \(T=5\%\) , the algorithm has the best performance. When \(T>5\%\) , the IGD + value of the algorithm increases gradually with the increase of T . This is because if the threshold value T is too large, the algorithm’s state will be adjusted frequently. Even if the population isn’t stable in one state (convergence, diversity, coordination), the algorithm will also be adjusted to other states. This isn’t conducive to improving the convergence and the diversity of the algorithm. The efficiency of the algorithm will also be affected. Therefore, for WFG3 test case with 10 objectives, \(T=5\%\) is the best parameter selection.

The corresponding relationship between IGD + value and T.

As shown in Fig.  18 , when \(std<0.7\) , the IGD + value of the algorithm decreases significantly with the increase of std . This is because if std is too small, the results of Gaussian sampling are too concentrated in the middle region, and the randomness of the sampling vector is weak, which isn’t conducive to the use of features and generation of diversified feature solutions. When \(std=0.7\) , the IGD + value of the algorithm is minimum. This shows that when \(std=0.7\) , the feature extraction operator performs best. When \(std>0.7\) , the IGD + value of the algorithm increases significantly with the increase of std . This is because if the std is too large, the result of Gaussian sampling is too scattered, the randomness of the sampling vector is strong, some components are easy to exceed the upper bound or lower bound, and some features are easy to be eliminated by the repair operator. Therefore, for WFG3 test case with 10 objectives, \(std=0.7\) is the best parameter selection.

The corresponding relationship between IGD + value and std.

Based on the above analysis of algorithm parameters, we think \(W=9, T=5\%, std=0.7\) are the best parameter combinations in WFG3 test case with 10 objectives. Further, we test the performance of the above parameter combinations in more test cases. The experimental results show that the above parameter combinations perform well in most test cases. Therefore, this paper sets the number of high-quality solutions \(W\) , the threshold value \(T\) and the standard deviation \(std\) to 9, 5% and 0.7, respectively.

Practical problem testing

This section mainly explores the performance of MOEA/TS algorithm in practical problems. The practical problem selected in this section is the industrial internet optimization problem based on the blockchain provided in reference 40 .

The industrial internet can support effective control of the physical world through a large amount of industrial data, but data security has always been a challenge due to various interconnections and accesses. Blockchain technology supports the security and privacy protection of industrial internet data with its trusted and reliable security mechanism. Fragmentation technology can help improve the overall throughput and scalability of the blockchain network. However, due to the uneven distribution of malicious nodes, the effectiveness of fragmentation is still challenging. In addition, the conflict between multiple industrial network indicators is also a problem we have to consider. Therefore, the industrial internet optimization problem based on blockchain is an important research problem.

In this section, the industrial internet optimization problem based on blockchain has the following 4 optimization objectives:

(1) Minimizing the shard invalidation probability (SIP);

(2) Minimizing the transmission delay (TD);

(3) Maximizing the throughput (TP);

(4) Minimizing the load of Malicious Nodes (LMN).

The research background of the industrial internet based on blockchain and the calculation formulas of these 4 objectives are detailed in reference 40 .

In this section, we set the population size to 220, the number of iterations to 300, and the number of function evaluations to 66000. We still use inverted generational distance plus (IGD+) to measure the performance of many-objective optimization algorithms. However, the real PF of the practical problem is unknown. Therefore, we run these algorithms many times to obtain the different non-dominated solution sets. The non-dominated union set of the different non-dominated solution sets is considered as the real PF. The relevant parameters of these algorithms are shown in Section 4.1.

In this section, each algorithm is executed 30 times to get the data as shown in Table 11 . As can be seen from Table 11 , MOEA/TS algorithm has absolute advantages. The performance of BiGE-BEW algorithm and MOEA/DG algorithm is good and similar. The performance of VMEF algorithm and MOEA/D algorithm in practical problems is obviously not as good as that in benchmark test functions. This is because the real PF of the practical problem is more complex. The performance of LSMaODE algorithm is close to that of MOEA/D algorithm. The performance of MaOEA/IT algorithm and MaOEA/IGD algorithm is the worst. Based on the above observations and analysis, we believe that MOEA/TS algorithm still has excellent performance and strong applicability in practical problems.

Considering that the solutions obtained by the many-objective optimization algorithms are the population, it is unrealistic to compare different network indicators of different algorithms intuitively. However, in practical applications, we only need to make choices according to the specific needs or preferences of users or enterprises. In this section, we first select the individuals with the largest throughput in each algorithm, and then compare the MOEA/TS algorithm with other algorithms on the basis of ensuring the maximum throughput. The network indicators obtained by these 8 algorithms are shown in Table 12 . As can be seen from Table 12 , in terms of SIP and TP, MOEA/TS algorithm has the best performance; In terms of TD, MOEA/TS algorithm ranks second; In terms of LMN, MOEA/TS algorithm ranks third. Therefore, we believe that the MOEA/TS algorithm has the best comprehensive performance in the industrial internet optimization problem based on blockchain, and various network indicators are at the forefront.

Based on the experimental analysis from Section 4.2 to Section 4.8, we can obtain the following conclusions:

(1) In the benchmark test cases, MOEA/TS algorithm is superior to the other 7 advanced many-objective optimization algorithms.

(2) MOEA/TS algorithm is more suitable for dealing with the MaOPs with more than 10 objectives.

(3) MOEA/TS algorithm can show excellent performance in different test function sets, and has strong universality and applicability.

(4) MOEA/TS algorithm has the best convergence and diversity, the strongest evolution ability and the fastest convergence speed.

(5) The 4 innovative parts of MOEA/TS algorithm are necessary and indispensable. The lack of any innovative parts will seriously affect the performance of MOEA/TS algorithm.

(6) MOEA/TS algorithm still has excellent performance and strong applicability in practical problems.

Summary and future work

Aiming at some difficulties in the many-objective optimization field, this paper proposes a many-objective evolutionary algorithm based on three states (MOEA/TS). Firstly, a feature extraction operator is proposed. The feature extraction operator is a feature extractor, which can extract the features of the high-quality solution set, and then assist the evolution of the current individual. Secondly, in terms of convergence maintenance, this paper doesn’t consider using domination relaxation technique, because the current domination relaxation technique still faces some problems. Based on Pareto front layer, this paper proposes the concept of “individual importance degree”. The importance degree of an individual can reflect the importance of the individual in the same Pareto front layer, so as to further distinguish the advantages and disadvantages of different individuals in the same front layer, and effectively solve the phenomenon of Pareto resistance. Then, in terms of diversity maintenance, this paper considers maintaining the diversity of the population in the objective space by repulsion field, so that the population can be evenly distributed on the real PF. Finally, a new concurrent algorithm framework is designed. In the framework, the algorithm is divided into three states, namely, convergence maintenance state, diversity maintenance state and coordination state. Each state focuses on a specific task. That is, the convergence maintenance state is responsible for improving the convergence of population; Diversity maintenance state is responsible for improving the diversity of population; the coordination state is responsible for coordinating the contradiction between diversity and convergence. The population can freely switch among these three states according to its own evolution. The experimental results show that MOEA/TS algorithm is superior to the other 7 advanced many-objective optimization algorithms. In addition, the effectiveness of the innovation parts is further verified.

However, MOEA/TS algorithm also has obvious defects: MOEA/TS algorithm isn’t good at dealing with test problems with narrow convergence curves or multiple local fronts. Therefore, in the future, we will further improve MOEA/TS algorithm, so that MOEA/TS algorithm can deal with test problems with narrow convergence curve or multiple local fronts. In addition, constrained MOPs and high-dimensional MOPs are also the focus of our future research.

Data availability

All data generated or analysed during this study are included in this published article.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 62362026 and 62162021; in part by the specific research fund of The Innovation Platform for Academicians of Hainan Province under grant YSPTZX202314; in part by the Key Project of Hainan Province under Grant ZDYF2023GXJS158.

National Natural Science Foundation of China, 62362026, 62362026, Specific research fund of The Innovation Platform for Academicians of Hainan Province, YSPTZX202314, YSPTZX202314, Key Project of Hainan Province, ZDYF2023GXJS158, ZDYF2023GXJS158.

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Conceptualization, J.L.Z., H.J.Z. and X.D.H.; Methodology, J.L.Z., H.J.Z. and X.D.H.; Software, J.L.Z., H.J.Z., H.H.Y. and H.S.F.; Validation, H.H.Y. and H.S.F.; Formal analysis, J.L.Z. and H.J.Z.; Investigation, H.H.Y. and H.S.F.; Resources, Q.L.Y.; Data curation, H.H.Y. and H.S.F.; Writing-original draft, J.L.Z.; Writing-review&editing, J.L.Z., H.J.Z., H.H.Y. and H.S.F.; Visualization, J.L.Z. and H.J.Z.; Supervision, H.H.Y. and H.S.F.; Project administration, J.L.Z. and Q.L.Y.; Funding acquisition, Q.L.Y.; All authors have read and agreed to the published version of the manuscript.

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Zhao, J., Zhang, H., Yu, H. et al. A many-objective evolutionary algorithm based on three states for solving many-objective optimization problem. Sci Rep 14 , 19140 (2024). https://doi.org/10.1038/s41598-024-70145-8

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