Dear Dr. Wu,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jul 31 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Alireza Goli

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at 

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and 

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Please note that PLOS ONE has specific guidelines on code sharing for submissions in which author-generated code underpins the findings in the manuscript. In these cases, all author-generated code must be made available without restrictions upon publication of the work. Please review our guidelines at https://journals.plos.org/plosone/s/materials-and-software-sharing#loc-sharing-code and ensure that your code is shared in a way that follows best practice and facilitates reproducibility and reuse.

3. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match. 

When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section.

Additional Editor Comments:

Reviewer 1:

Does the proposed Multi-Objective Dung Beetle Optimization (MODBO) algorithm incorporate competitive and neighborhood mechanisms for solving multi-objective problems?

How does the introduction of fast, non-dominated sorting enhance the Dung Beetle Optimization Algorithm's ability to solve multi-objective optimization problems?

What role does the Competition mechanism play in guiding global optimal search within the MODBO algorithm?

In the introduction, you need to connect the state of the art to your paper goals. Please follow the literature review by a clear and concise state of the art analysis. This should clearly show the knowledge gaps identified and link them to your paper goals. Please reason both the novelty and the relevance of your paper goals. Clearly discuss what the previous studies that you are referring to. What are the Research Gaps/Contributions? Please note that the paper may not be considered further without a clear research gap and novelty of the study.

Literature Review has the chance to be further improved: it seems that the authors have made the retrospection. However, via the review, what issues should be addressed? What is the current specific knowledge gap? What implication can be referred to? The above questions should be answered. Authors need to propose their study and compare it with A Multi Echelon Location-Routing-Inventory Model for a Supply Chain Network: NSGA II and Multi-Objective Whale Optimization Algorithm, A new modified social engineering optimizer algorithm for engineering applications

How does the Neighborhood mechanism assist in guiding local optimal value search in the MODBO algorithm?

What purpose does the external archive serve within the MODBO algorithm, and how does it contribute to achieving optimality?

How was the effectiveness of the MODBO algorithm evaluated, and what specific benchmarks were used in comparison with other algorithms?

Can you explain how the MODBO algorithm performed when tested on engineering problems, such as the 3D sensor deployment problem?

What are the key findings from the comparison of MODBO with other algorithms in terms of solving complex multi-objective problems, both in terms of convergence and distribution

Reviewer 2:

The article titled "Multi-objective dung beetle optimization algorithm: A novel algorithm for solving complex multi-objective optimization problems" introduces a new optimization algorithm, MODBO, which builds upon the existing dung beetle optimization algorithm (DBO) to address multi-objective problems (MOPs). This novel algorithm incorporates mechanisms such as competitive and neighborhood mechanisms, non-dominated sorting, and external archiving to enhance its capability to find optimal solutions in complex multi-objective spaces.

Key Components and Innovations:

1. Non-Dominated Sorting and External Archiving

Non-dominated sorting is a critical technique in multi-objective optimization that classifies solutions based on Pareto dominance. Solutions that are not dominated by any other solution form a Pareto front. MODBO integrates non-dominated sorting to efficiently manage and evolve the population of solutions. Additionally, an external archive is used to store the best non-dominated solutions found so far, ensuring that the algorithm maintains a diverse set of high-quality solutions across iterations.

2. Competition and Neighborhood Mechanisms

To enhance the search capabilities of the DBO algorithm, MODBO introduces two significant mechanisms:

• Competition Mechanism: This guides particles towards the global optimal solution by encouraging competition among particles. It helps in maintaining the diversity of the solutions and prevents premature convergence to suboptimal solutions.

• Neighborhood Mechanism: This mechanism focuses on local optimization by allowing particles to explore their immediate surroundings more thoroughly. It ensures that the algorithm can fine-tune solutions to achieve better local optimality.

3. Performance Evaluation and Benchmarking

The MODBO algorithm's performance is evaluated against nine established algorithms using the CEC2020 benchmark suite. Additionally, a practical application of MODBO is demonstrated through the 3D sensor deployment problem, which showcases its effectiveness in real-world scenarios.

Strengths:

1. Innovative Mechanisms for Improved Search

The incorporation of competition and neighborhood mechanisms addresses common issues in optimization algorithms, such as maintaining a balance between exploration and exploitation. These mechanisms ensure that the algorithm does not get trapped in local optima and can explore the solution space more effectively.

2. Comprehensive Benchmarking

The use of CEC2020 benchmark problems provides a robust and standardized means of evaluating the algorithm's performance. This comparison with established algorithms highlights the strengths and areas of improvement for MODBO.

3. Practical Application Demonstration

Applying MODBO to the 3D sensor deployment problem illustrates its practical utility and effectiveness in solving real-world problems. This practical validation adds credibility to the algorithm's theoretical advancements.

Weaknesses:

1. Complexity of Mechanisms

While the competition and neighborhood mechanisms enhance the algorithm's performance, they also add complexity. Implementing and tuning these mechanisms might require significant computational resources and expert knowledge, which could limit the algorithm's accessibility and usability.

2. Limited Scope of Applications Demonstrated

Although the 3D sensor deployment problem is a valid application, demonstrating MODBO's effectiveness across a wider range of practical problems would strengthen the claims about its versatility and robustness.

3. Dependence on Parameters

Like many optimization algorithms, MODBO's performance is highly dependent on the proper setting of various parameters (e.g., competition and neighborhood coefficients). Finding the optimal parameters can be challenging and may require extensive experimentation.

Suggestions for Improvement:

1. Simplification of Mechanisms

Exploring ways to simplify the competition and neighborhood mechanisms without significantly compromising performance could make the algorithm more accessible. For instance, adaptive mechanisms that automatically adjust parameters during the search process could reduce the need for manual tuning.

2. Broader Application Testing

Extending the evaluation of MODBO to a wider range of real-world problems would provide more evidence of its generalizability and robustness. Including diverse applications from different domains such as logistics, finance, and bioinformatics would strengthen the case for MODBO's versatility.

3. Parameter Sensitivity Analysis

Conducting a detailed sensitivity analysis of the algorithm's parameters would provide valuable insights into their impact on performance. This could help in developing guidelines or heuristics for setting parameters, making the algorithm more user-friendly.

4. Integration with Machine Learning Techniques

Integrating MODBO with machine learning techniques could further enhance its performance. For example, using machine learning to predict the effectiveness of certain parameter settings or to guide the search process could improve both the efficiency and the outcomes of the optimization.

Conclusion:

The Multi-Objective Dung Beetle Optimization Algorithm (MODBO) represents a significant advancement in the field of multi-objective optimization. By leveraging innovative mechanisms and robust benchmarking, it demonstrates strong potential for solving complex optimization problems. However, addressing its complexity, expanding its application scope, and optimizing its parameters could further enhance its usability and effectiveness.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: No

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

Reviewer #1: Does the proposed Multi-Objective Dung Beetle Optimization (MODBO) algorithm incorporate competitive and neighborhood mechanisms for solving multi-objective problems?

How does the introduction of fast, non-dominated sorting enhance the Dung Beetle Optimization Algorithm's ability to solve multi-objective optimization problems?

What role does the Competition mechanism play in guiding global optimal search within the MODBO algorithm?

In the introduction, you need to connect the state of the art to your paper goals. Please follow the literature review by a clear and concise state of the art analysis. This should clearly show the knowledge gaps identified and link them to your paper goals. Please reason both the novelty and the relevance of your paper goals. Clearly discuss what the previous studies that you are referring to. What are the Research Gaps/Contributions? Please note that the paper may not be considered further without a clear research gap and novelty of the study.

Literature Review has the chance to be further improved: it seems that the authors have made the retrospection. However, via the review, what issues should be addressed? What is the current specific knowledge gap? What implication can be referred to? The above questions should be answered. Authors need to propose their study and compare it with A Multi Echelon Location-Routing-Inventory Model for a Supply Chain Network: NSGA II and Multi-Objective Whale Optimization Algorithm, A new modified social engineering optimizer algorithm for engineering applications

How does the Neighborhood mechanism assist in guiding local optimal value search in the MODBO algorithm?

What purpose does the external archive serve within the MODBO algorithm, and how does it contribute to achieving optimality?

How was the effectiveness of the MODBO algorithm evaluated, and what specific benchmarks were used in comparison with other algorithms?

Can you explain how the MODBO algorithm performed when tested on engineering problems, such as the 3D sensor deployment problem?

What are the key findings from the comparison of MODBO with other algorithms in terms of solving complex multi-objective problems, both in terms of convergence and distribution

Reviewer #2: The article titled "Multi-objective dung beetle optimization algorithm: A novel algorithm for solving complex multi-objective optimization problems" introduces a new optimization algorithm, MODBO, which builds upon the existing dung beetle optimization algorithm (DBO) to address multi-objective problems (MOPs). This novel algorithm incorporates mechanisms such as competitive and neighborhood mechanisms, non-dominated sorting, and external archiving to enhance its capability to find optimal solutions in complex multi-objective spaces.

Key Components and Innovations:

1. Non-Dominated Sorting and External Archiving

Non-dominated sorting is a critical technique in multi-objective optimization that classifies solutions based on Pareto dominance. Solutions that are not dominated by any other solution form a Pareto front. MODBO integrates non-dominated sorting to efficiently manage and evolve the population of solutions. Additionally, an external archive is used to store the best non-dominated solutions found so far, ensuring that the algorithm maintains a diverse set of high-quality solutions across iterations.

2. Competition and Neighborhood Mechanisms

To enhance the search capabilities of the DBO algorithm, MODBO introduces two significant mechanisms:

• Competition Mechanism: This guides particles towards the global optimal solution by encouraging competition among particles. It helps in maintaining the diversity of the solutions and prevents premature convergence to suboptimal solutions.

• Neighborhood Mechanism: This mechanism focuses on local optimization by allowing particles to explore their immediate surroundings more thoroughly. It ensures that the algorithm can fine-tune solutions to achieve better local optimality.

3. Performance Evaluation and Benchmarking

The MODBO algorithm's performance is evaluated against nine established algorithms using the CEC2020 benchmark suite. Additionally, a practical application of MODBO is demonstrated through the 3D sensor deployment problem, which showcases its effectiveness in real-world scenarios.

Strengths:

1. Innovative Mechanisms for Improved Search

The incorporation of competition and neighborhood mechanisms addresses common issues in optimization algorithms, such as maintaining a balance between exploration and exploitation. These mechanisms ensure that the algorithm does not get trapped in local optima and can explore the solution space more effectively.

2. Comprehensive Benchmarking

The use of CEC2020 benchmark problems provides a robust and standardized means of evaluating the algorithm's performance. This comparison with established algorithms highlights the strengths and areas of improvement for MODBO.

3. Practical Application Demonstration

Applying MODBO to the 3D sensor deployment problem illustrates its practical utility and effectiveness in solving real-world problems. This practical validation adds credibility to the algorithm's theoretical advancements.

Weaknesses:

1. Complexity of Mechanisms

While the competition and neighborhood mechanisms enhance the algorithm's performance, they also add complexity. Implementing and tuning these mechanisms might require significant computational resources and expert knowledge, which could limit the algorithm's accessibility and usability.

2. Limited Scope of Applications Demonstrated

Although the 3D sensor deployment problem is a valid application, demonstrating MODBO's effectiveness across a wider range of practical problems would strengthen the claims about its versatility and robustness.

3. Dependence on Parameters

Like many optimization algorithms, MODBO's performance is highly dependent on the proper setting of various parameters (e.g., competition and neighborhood coefficients). Finding the optimal parameters can be challenging and may require extensive experimentation.

Suggestions for Improvement:

1. Simplification of Mechanisms

Exploring ways to simplify the competition and neighborhood mechanisms without significantly compromising performance could make the algorithm more accessible. For instance, adaptive mechanisms that automatically adjust parameters during the search process could reduce the need for manual tuning.

2. Broader Application Testing

Extending the evaluation of MODBO to a wider range of real-world problems would provide more evidence of its generalizability and robustness. Including diverse applications from different domains such as logistics, finance, and bioinformatics would strengthen the case for MODBO's versatility.

3. Parameter Sensitivity Analysis

Conducting a detailed sensitivity analysis of the algorithm's parameters would provide valuable insights into their impact on performance. This could help in developing guidelines or heuristics for setting parameters, making the algorithm more user-friendly.

4. Integration with Machine Learning Techniques

Integrating MODBO with machine learning techniques could further enhance its performance. For example, using machine learning to predict the effectiveness of certain parameter settings or to guide the search process could improve both the efficiency and the outcomes of the optimization.

Conclusion:

The Multi-Objective Dung Beetle Optimization Algorithm (MODBO) represents a significant advancement in the field of multi-objective optimization. By leveraging innovative mechanisms and robust benchmarking, it demonstrates strong potential for solving complex optimization problems. However, addressing its complexity, expanding its application scope, and optimizing its parameters could further enhance its usability and effectiveness.

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #1: No

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org . Please note that Supporting Information files do not need this step.

Dear Dr. Tian,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

Reviewer 1:

Thank you for addressing all comments. The revisions improve the paper significantly. I appreciate your thorough response.

Reviewer 2:

Dear Author,

I have reviewed the paper you provided, and I believe a few areas could benefit from further refinement and clarification. While the article has seen some improvements, there are still opportunities to enhance the overall quality through thoughtful discussion and review of the technical and substantive aspects.

Specifically, I would suggest considering the incorporation of the following concepts, which appear to be relevant to the subject matter:

1. The combined use of whale and moth-flame optimization algorithms.

This approach could potentially offer refined optimization techniques that could strengthen the analytical framework.

2. An optimal task scheduling method in Fog-IoT networks, utilizing a combination of AO and Whale Optimization Algorithm (WOA).

This combination may provide valuable insights into improving the efficiency and effectiveness of task management within the Fog-IoT environment.

I believe the inclusion and thorough examination of these concepts would contribute to a more comprehensive and well-rounded article. By addressing these technical and distinguished issues, you can elevate the quality of the work and ensure that the audience gains a deeper understanding of the subject matter.

I am happy to discuss these suggestions in further detail or provide any additional support you may require. Please let me know if you have any questions or if there is anything else I can assist with.

Best regards,

==============================

Please submit your revised manuscript by Oct 03 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Alireza Goli

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Reviewer 1:

Thank you for addressing all comments. The revisions improve the paper significantly. I appreciate your thorough response.

Reviewer 2:

Dear Author,

I have reviewed the paper you provided, and I believe a few areas could benefit from further refinement and clarification. While the article has seen some improvements, there are still opportunities to enhance the overall quality through thoughtful discussion and review of the technical and substantive aspects.

Specifically, I would suggest considering the incorporation of the following concepts, which appear to be relevant to the subject matter:

1. The combined use of whale and moth-flame optimization algorithms.

This approach could potentially offer refined optimization techniques that could strengthen the analytical framework.

2. An optimal task scheduling method in Fog-IoT networks, utilizing a combination of AO and Whale Optimization Algorithm (WOA).

This combination may provide valuable insights into improving the efficiency and effectiveness of task management within the Fog-IoT environment.

I believe the inclusion and thorough examination of these concepts would contribute to a more comprehensive and well-rounded article. By addressing these technical and distinguished issues, you can elevate the quality of the work and ensure that the audience gains a deeper understanding of the subject matter.

I am happy to discuss these suggestions in further detail or provide any additional support you may require. Please let me know if you have any questions or if there is anything else I can assist with.

Best regards,

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: No

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

Reviewer #1: Thank you for addressing all comments. The revisions improve the paper significantly. I appreciate your thorough response.

Reviewer #2: Dear Author,

I have reviewed the paper you provided, and I believe a few areas could benefit from further refinement and clarification. While the article has seen some improvements, there are still opportunities to enhance the overall quality through thoughtful discussion and review of the technical and substantive aspects.

Specifically, I would suggest considering the incorporation of the following concepts, which appear to be relevant to the subject matter:

1. The combined use of whale and moth-flame optimization algorithms.

This approach could potentially offer refined optimization techniques that could strengthen the analytical framework.

2. An optimal task scheduling method in Fog-IoT networks, utilizing a combination of AO and Whale Optimization Algorithm (WOA).

This combination may provide valuable insights into improving the efficiency and effectiveness of task management within the Fog-IoT environment.

I believe the inclusion and thorough examination of these concepts would contribute to a more comprehensive and well-rounded article. By addressing these technical and distinguished issues, you can elevate the quality of the work and ensure that the audience gains a deeper understanding of the subject matter.

I am happy to discuss these suggestions in further detail or provide any additional support you may require. Please let me know if you have any questions or if there is anything else I can assist with.

Best regards,

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #1: No

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org

Dear Dr. Tian,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

Editor:

please consider the following comments:

1. Page 3: The introduction of the term "multi-objective" could be clarified earlier in the text to enhance reader understanding.

2. Page 4: The phrase "leveraging the effectiveness of the dung beetle optimization algorithm" should be rephrased for clarity and conciseness.

3. Page 5: Consider providing a brief explanation of the "Competition mechanism" and "Neighborhood mechanism" to ensure all readers grasp their significance.

4. Page 6: There is a typographical error in the sentence discussing "non-dominated sorting"; please correct it to maintain professionalism.

5. Page 7: The reference to "CEC2020" should include a brief description of what it entails for readers unfamiliar with it.

6. Page 8: The transition between sections could be smoother; consider adding a sentence to link the discussion of algorithms to the results presented.

7. Page 9: The table formatting in Table 2 is inconsistent; ensure all columns are aligned for better readability.

8. Page 10: The experimental results could benefit from a more detailed explanation of the metrics used for evaluation.

9. Page 11: The conclusion could be strengthened by summarizing the implications of the findings more explicitly.

10. Page 12: Ensure all figures are referenced in the text before they appear to guide the reader effectively through the manuscript.

==============================

Please submit your revised manuscript by Oct 18 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Alireza Goli

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Editor:

please consider the following comments:

1. Page 3: The introduction of the term "multi-objective" could be clarified earlier in the text to enhance reader understanding.

2. Page 4: The phrase "leveraging the effectiveness of the dung beetle optimization algorithm" should be rephrased for clarity and conciseness.

3. Page 5: Consider providing a brief explanation of the "Competition mechanism" and "Neighborhood mechanism" to ensure all readers grasp their significance.

4. Page 6: There is a typographical error in the sentence discussing "non-dominated sorting"; please correct it to maintain professionalism.

5. Page 7: The reference to "CEC2020" should include a brief description of what it entails for readers unfamiliar with it.

6. Page 8: The transition between sections could be smoother; consider adding a sentence to link the discussion of algorithms to the results presented.

7. Page 9: The table formatting in Table 2 is inconsistent; ensure all columns are aligned for better readability.

8. Page 10: The experimental results could benefit from a more detailed explanation of the metrics used for evaluation.

9. Page 11: The conclusion could be strengthened by summarizing the implications of the findings more explicitly.

10. Page 12: Ensure all figures are referenced in the text before they appear to guide the reader effectively through the manuscript.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #2: I Don't Know

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #2: Yes

**********

Reviewer #2: (No Response)

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org

Dear Dr. Tian,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Dec 06 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Khan Bahadar Khan, Ph.D

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #3: (No Response)

Reviewer #4: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #3: Partly

Reviewer #4: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #3: Yes

Reviewer #4: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #3: Yes

Reviewer #4: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #3: No

Reviewer #4: No

**********

Reviewer #3: I appreciate the opportunity to review this manuscript, which presents the Multi-Objective Dung Beetle Optimization Algorithm (MODBO). The authors propose a novel approach to addressing complex multi-objective optimization problems (MOPs) by enhancing the original Dung Beetle Optimization (DBO) algorithm. Below are my comments and suggestions for improvement:

Novelty and Contribution:

The integration of competitive and neighborhood mechanisms into MODBO is commendable. The comparative analysis against established algorithms adds considerable value to the study.

Methodological Rigor:

The methodology is generally solid and well detailed.

Literature Positioning:

The introduction provides a comprehensive overview of related works but lacks clarity in organization. Currently, it reads as a list rather than a cohesive narrative. A more structured presentation that clearly articulates how this study fits within the existing literature would strengthen the manuscript.

I must express my concern regarding the significant similarities between this manuscript and the foundational work titled "Dung beetle optimizer: a new meta-heuristic algorithm for global optimization." and derived works titled "Multi-Strategy Improved Dung Beetle Optimization Algorithm and Its Applications". Specific images (e.g., Image 1 and Image 3) and certain phrasings, such as the discussion surrounding the "No Free Lunch Theorem", page 3, and the description of the DBO algorithm, sec 2.1, reflect similar content and structure.

Given these overlaps, it is crucial that the authors clearly distinguish their contributions from this foundational work to avoid issues of dual publication or academic integrity.

Clarity and Writing Quality:

Overall, the writing is clear, but results and conclusions sections could benefit from reorganization to enhance readability. Emphasizing key findings in the conclusion would provide a clearer takeaway for readers and highlight the study’s significance.

Additionally, ensuring that all acronyms (e.g., MOEAs) are defined upon first use would improve accessibility for all readers. Typos are still present around the text.

Figures and Tables:

While the figures and tables are informative, some appear raw. I recommend highlighting aggregated results and moving detailed data to an appendix to improve readability. It would also be beneficial to ensure that all figures have descriptive captions and that axes are appropriately labeled.

Limitations:

Addressing potential limitations of the research in the conclusion would provide a more balanced view and further strengthen the manuscript.

Overall Recommendation

In conclusion, I believe this manuscript makes a valuable and solid contribution to the field of optimization algorithms and has great potential for practical applications. I recommend acceptance with minor revisions, as I am confident that addressing the points mentioned will significantly enhance the quality and clarity of the work.

Thank you for considering my comments. I appreciate the effort the authors have put into this research and look forward to seeing how it develops.

Reviewer #4: 1. There exists already MODBO using non dominated sorting algorithm in the literature. Go through the following link https://doi.org/10.1016/j.heliyon.2024.e37286. How you method is different and what is need of proposing the same method.

2. Instead of using CEC 2020 test suit can the authors use the recent test suits for instance CEC 2022.

3. The comparative study is messing in the manuscript with respect to the technique. It is advised to test and compare with the above reference if both are different.

4. There is non-parametric test provided in the manuscript.

**********

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Reviewer #3: No

Reviewer #4: No

**********

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While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org

Attachments

Attachment

Submitted filename: Review.pdf

Dear Dr. Tian,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jan 15 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Khan Bahadar Khan, Ph.D

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #3: (No Response)

Reviewer #4: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #3: (No Response)

Reviewer #4: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #3: (No Response)

Reviewer #4: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #3: (No Response)

Reviewer #4: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #3: (No Response)

Reviewer #4: Yes

**********

Reviewer #3: Thank you for your responses to my previous comments. However, I find that several of the revisions do not adequately address the concerns raised. Below, I provide a detailed assessment of your responses and additional feedback for improvement:

Comment 1: Organization of the Introduction

The introduction remains largely a list of related works without a cohesive structure or clear articulation of how this study fits into the broader context. While you mentioned adding a guiding narrative, I expected a more substantial restructuring of the section.

Further suggestion: The introduction should be reorganized, requiring changes to its structure. Focus on clearly highlighting gaps in the existing literature and explaining how your study addresses them.

Comment 2: Potential Overlaps with Foundational Work

The response acknowledges similarities with foundational works but does not address the issue of textual overlap or reliance on figures from prior studies.

Further suggestion: It is critical to either rephrase sections with significant textual similarity in your own words or explicitly cite the source within the text. For any figures derived from previous works, please provide alternative visual representations or clearly attribute them to the original source.

Comment 3: Clarity and Readability of Results and Conclusions

I do not see a significant improvement in the clarity or readability of the results and conclusions sections.

Comment 4: Research Limitations

The limitations described focus on the problem's difficulty but fail to address trade-offs or potential weaknesses in the algorithm itself. A critical self-assessment is missing, which undermines the balance of the discussion.

Further suggestion: Please provide a thorough discussion of the algorithm’s limitations, such as potential trade-offs, scalability, or computational constraints. A balanced evaluation will enhance the transparency and scientific rigor of your work.

Conclusion

While I appreciate the effort to address the comments, the revisions made thus far do not sufficiently improve the manuscript. I encourage you to carefully consider the above suggestions to strengthen the introduction, clarify your contributions, and enhance the overall readability and quality of the manuscript.

Reviewer #4: All Queries asked by the reviewer have been answered satisfactory by the authors. It is advised to keep the code publicly after acceptance.

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

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Reviewer #3: No

Reviewer #4: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org

Dear Dr. Tian,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Mar 13 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Khan Bahadar Khan, Ph.D

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #3: All comments have been addressed

Reviewer #5: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #3: Yes

Reviewer #5: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #3: Yes

Reviewer #5: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #3: Yes

Reviewer #5: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #3: No

Reviewer #5: Yes

**********

Reviewer #3: I appreciate the detailed effort the authors have made to address my comments. The manuscript has improved substantially in terms of structure.

While the manuscript is now suitable for publication, I suggest a careful review of the English language to improve readability. There are some awkward phrasings and grammatical issues that could be polished. For instance, in the introduction:

"Although many algorithms have been proposed and performed well in many real-world problems, it is worth mentioning."

I recommend rephrasing sentences like this to enhance clarity.

Overall, the authors have addressed all my concerns, and I have no further objections.

Reviewer #5: • Introduction:

• The introduction is now more organized and provides better context. However, it would benefit from slightly more focus on how your proposed algorithm directly addresses the gaps in the existing methods.

• Algorithm Explanation:

• The explanations of the Competition and Neighborhood mechanisms are clear and well-structured. No further changes are needed here.

• Results and Discussion:

• The results section is comprehensive and the statistical analyses are robust. However, a brief discussion on why MODBO performs slightly worse in some cases would improve balance and clarity.

• Figures and Tables:

• The figures and tables are clear and well-presented. Ensure that all figure captions briefly explain their relevance to the results.

• Limitations and Future Work:

• The updated limitations section is much improved. No additional changes are needed, but you might briefly mention specific future applications of MODBO to strengthen the conclusion.

• Language and Style:

• The language has improved, but a few minor typos remain (e.g., "rolling shithouse" in Section 2.1.4). Please fix these to maintain professionalism.

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #3: No

Reviewer #5: Yes:  Anas Amaireh

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org

Attachments

Attachment

Submitted filename: PONE-D-24-09818_R5.docx

Dear Dr. Tian,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Apr 18 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Khan Bahadar Khan, Ph.D

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #3: All comments have been addressed

Reviewer #5: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #3: Yes

Reviewer #5: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #3: Yes

Reviewer #5: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #3: Yes

Reviewer #5: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #3: Yes

Reviewer #5: Yes

**********

Reviewer #3: The author’s previous response appears to be directed toward Reviewer 5, based on its content. Please ensure that responses are correctly addressed to maintain clarity in the review process.

Reviewer #5: The manuscript presents a novel Multi-objective Dung Beetle Optimization Algorithm (MODBO), extending the traditional Dung Beetle Optimization (DBO) to handle multi-objective problems. The proposed algorithm incorporates competition and neighborhood mechanisms to enhance global and local search capabilities, respectively. The authors validate MODBO on CEC2020 benchmark functions and apply it to a 3D wireless sensor deployment problem, demonstrating its effectiveness compared to nine existing algorithms. The study provides a strong theoretical foundation, a well-structured methodology, and a comprehensive performance evaluation.

While the work is innovative and impactful, there are several areas that require improvement before publication.

Major comments:

• MODBO outperforms most algorithms but is surpassed by NSGA-II and MO_Ring_PSO_SCD in some test cases. The paper attributes MO_Ring_PSO_SCD’s success to ring topology and congestion distance but lacks a detailed explanation of MODBO’s weaknesses. A deeper analysis of these cases is needed to understand its limitations and potential improvements.

• MODBO introduces competition and neighborhood mechanisms, but the impact of key hyperparameters (competition intensity, archive size, neighborhood radius) is not analyzed. A sensitivity study should be included to clarify their effects on performance.

• The explanation of MODBO’s workflow (Figure 5) is detailed but lacks pseudocode. Adding structured pseudocode would improve clarity and reproducibility.

• The limitations section is well-written but could suggest solutions. Future work should explore hybridization with deep learning for parameter tuning, adaptation to dynamic environments for real-time optimization, and parallelization techniques for large-scale problems.

Minor comments:

• Some sentences are awkwardly phrased or redundant. For example, "rolling shithouse" in Section 2.1.4 appears to be a typographical error and should be corrected. A thorough proofreading is needed to improve clarity and professionalism.

• Some figures, such as Figure 5, would benefit from clearer annotations and more descriptive captions. Figure descriptions should be revised to better explain their purpose.

• Some mathematical symbols and equations are used without proper definitions. All symbols and variables should be introduced before their first occurrence to ensure consistency.

The manuscript presents a significant contribution to multi-objective optimization. However, a more detailed justification of performance differences, parameter sensitivity analysis, and minor textual refinements are needed before acceptance.

**********

what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #3: No

Reviewer #5: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org

Dear Dr. Tian,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jul 16 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Khan Bahadar Khan, Ph.D

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #3: All comments have been addressed

Reviewer #6: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #3: Yes

Reviewer #6: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #3: Yes

Reviewer #6: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #3: Yes

Reviewer #6: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #3: Yes

Reviewer #6: Yes

**********

Reviewer #3: All my concerns have been tackled in past reviews and I don't have any additional comment for this statement.

Reviewer #6: After a thorough review of the latest (7th) revision of the manuscript and careful cross-checking with the suggestions from the editor and Reviewer #3, I confirm that the authors have satisfactorily addressed all critical feedback. However, I have some more suggestions to improve the quality of the manuscript.

The paper presents a potentially impactful algorithm, but the presentation suffers from poor structure, redundancy, and weak articulation of novelty. A clearer motivation, concise and focused narrative, improved grammar, and critical engagement with related work would significantly enhance its scholarly quality. In introduction section, several paragraphs repeat similar information about algorithm types and challenges. Start with a strong motivation and define the research gap clearly. Then review related work critically rather than just descriptively. Instead of listing algorithms, explain trends and challenges in existing MOP algorithms, especially in local vs. global search strategies.

Revise the abstract for brevity and grammar. Focus on the key contributions, methodology, and results in a structured format: problem → method → results → implications.

In section II, phrases like “different solutions will have different effects on particle guidance” and “to ensure the diversity of the population” are mentioned multiple times with little added value. This should be streamlined for conciseness and precision.

Add formal update rules or pseudocode describing how particle positions are changed based on winners. Explain with clear criteria how the "smaller angle" is computed and why it is a good measure.

Clarify the update rule with a step-by-step description or equation. Also, explain why a ring topology was chosen over other neighborhood structures (e.g., global best, von Neumann, etc.).

Replace the current block with a numbered algorithm pseudocode box. Clearly label which steps correspond to which sub-behaviors of DBO (e.g., Ball Rolling → Step 4).

Provide algorithmic insight into why MODBO performs better on certain functions (e.g., MMF1–MMF5) and worse on others (e.g., MMF10 or MMF13). Discuss the impact of problem features (like modality, deception, or Pareto set geometry) on MODBO’s behavior.

Apply appropriate non-parametric statistical tests across benchmark problems. Include tables or plots highlighting significance markers (e.g., symbols to denote better/similar/worse performance).

Boldface or color-code the best-performing results for each benchmark function. Add a summary row or figure showing the number of wins/losses/ties of MODBO against competitors. Replace some tables with visual summaries like bar plots or radar charts to enhance readability.

Briefly justify why the chosen MODBO parameters (e.g., α, β, S) are suitable. If manual tuning was done, explain the tuning approach (e.g., grid search, trial-and-error). Clarify whether parameters for other algorithms were adopted from literature or optimized independently.

The introduction to Section 4 begins abruptly, without clearly establishing why sensor deployment is an important test case or how it challenges conventional optimization methods. Begin Section 4 with a concise problem statement and justification: why is 3D sensor deployment a suitable test for MODBO?

Related work is mentioned with minimal detail and without analytical depth or comparative evaluation. For example, how the swarm intelligence strategies used by others differ structurally from MODBO remains unclear.

Clearly define all variables and equations with proper mathematical notation and explain their significance.

Binary perception modeling is assumed (i.e., 0 or 1 perception), which ignores signal strength attenuation or probabilistic sensing models common in WSN literature. Consider replacing binary coverage models with probabilistic sensing functions, as these better reflect physical reality.

Coverage is considered purely geometrically, without integrating communication range, power limits, or data routing, which are central to realistic WSN deployment. Extend the model to include communication constraints or at least acknowledge them as future considerations.

Clarify how terrain surfaces are modeled and how the algorithm handles line-of-sight obstruction. Consider incorporating ray-tracing or voxel-based visibility models for more accurate detection of blind spots. Provide a formal algorithmic procedure (e.g., pseudocode or flowchart) for determining blind zones.

Equations (17) and (18) are stated without context. Why is coverage maximization and node minimization sufficient? Are other objectives (energy, lifetime, redundancy) not relevant?

The optimization problem lacks constraints typically found in real WSN deployments, such as deployment cost limits, maximum allowable latency, or maximum power usage. Discuss how trade-offs are handled (e.g., Pareto front analysis) and consider adding constraints like maximum allowable energy per node.

How is the 3D terrain generated? What kinds of obstacles or elevation variations are present? Provide detailed terrain descriptions and explain the simulation setup, including the number of runs, random seeds, and environmental parameters.

Only a single experiment appears to be conducted, comparing random placement and MODBO. There is no statistical validation, confidence intervals, or multiple trials. Present coverage results with statistical metrics, e.g., average ± standard deviation over multiple trials.

The figures are referenced (Figs. 13–15), but there's no in-depth discussion of spatial distribution, blind spots, or redundant coverage. Add comparative visualizations (e.g., heatmaps, 3D terrain maps) showing the distribution and performance of sensors.

Thorough proofreading and possibly consultation with a native English speaker or language editor is advised. Carefully proofread and typeset all equations and variables.

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Reviewer #3: No

Reviewer #6: Yes:  Vijay Govindarajan

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Multi-objective dung beetle optimization algorithm: A novel algorithm for solving complex multi-objective optimization problems

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Reviewer #7: No

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