HPSBA: A Modified Hybrid Framework with Convergence Analysis for Solving Wireless Sensor Network Coverage Optimization Problem
Round 1
Reviewer 1 Report
The research paper is extremely well written and presented, and I have no doubt that it will leave a huge impact on the readers.
Figure 8, which is supposed to represent the node convergence optimization is not clear, and the explanation of it is not well conveyed, this is the sole problem of the paper.
Author Response
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We appreciate the reviewer for his/her precious and valuable comments that help us improve the quality of the paper. In the revised paper and the response letter, the mentioned issues have been fully considered and addressed.
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Open Review
( ) I would not like to sign my review report
(x) I would like to sign my review report
English language and style
( ) English very difficult to understand/incomprehensible
( ) Extensive editing of English language and style required
(x) Moderate English changes required
( ) English language and style are fine/minor spell check required
( ) I don't feel qualified to judge about the English language and style
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Does the introduction provide sufficient background and include all relevant references? |
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Are all the cited references relevant to the research? |
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Comments and Suggestions for Authors
The research paper is extremely well written and presented, and I have no doubt that it will leave a huge impact on the readers.
Response:
Thank you for your recognition of our paper.
- Figure 8, which is supposed to represent the node convergence optimization is not clear, and the explanation of it is not well conveyed, this is the sole problem of the paper.
Response:
We have adjusted the size of the Figure 8, and added more explanation in the revised manuscript.
Reviewer 2 Report
This paper presents a novel hybrid particle swarm butterfly algorithm (HPSBA) on the basis of HPSOBOA.
I think the paper is well written and organised. The evaluation is extensive and well formulated.
I have only a few minor comments. The introduction is difficult to follow. A large list of bio-inspired algorithms is listed, but no additional or relevant information is provided for the context of the paper. I think the introduction could be simplified. The text in Tables 2, 4 and 5 is too small.
Author Response
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Thanks a lot for the invaluable comments and suggestions, these suggestions are very valuable for our paper, and we carefully revise the manuscript according to these suggestions.
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Open Review
Open Review
( ) I would not like to sign my review report
(x) I would like to sign my review report
English language and style
( ) English very difficult to understand/incomprehensible
( ) Extensive editing of English language and style required
( ) Moderate English changes required
( ) English language and style are fine/minor spell check required
(x) I don't feel qualified to judge about the English language and style
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Can be improved |
Must be improved |
Not applicable |
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Does the introduction provide sufficient background and include all relevant references? |
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Are all the cited references relevant to the research? |
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Is the research design appropriate? |
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Are the methods adequately described? |
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Are the results clearly presented? |
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Are the conclusions supported by the results? |
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Comments and Suggestions for Authors
This paper presents a novel hybrid particle swarm butterfly algorithm (HPSBA) on the basis of HPSOBOA.
I think the paper is well written and organised. The evaluation is extensive and well formulated.
Response:
Thank you for your recognition of our paper.
I have only a few minor comments. The introduction is difficult to follow. A large list of bio-inspired algorithms is listed, but no additional or relevant information is provided for the context of the paper. I think the introduction could be simplified. The text in Tables 2, 4 and 5 is too small.
Response:
The logical idea of the introduction is as follows. We firstly review the existing research status and classification of population intelligence algorithms. Secondly, we introduce the research status of the application task, i.e., the application of population intelligence algorithms in WSN coverage optimization problems. Finally, we introduce our research motivation and innovation, and summarize the basic structure of the article. We have resized the text size of Tables 2, 4 and 5 of the revised manuscript.
Reviewer 3 Report
1. Abstract. The extensive initial fragment is a general introduction, but it contains too little information about the research problem and the scientific problem solved. The abstract should be reedited and supplemented as it does not fully reflect the substantive content of the publication.
2. Figure 4 provides little cognitive information. Perhaps it can be presented in a different form?
3. Figure 6. How are the indices "new" and "i+new" determined for the quantities F and X? Is fbest the same as Fbest?
4. Table 4 and Table 5. The precision of the listed parameter values may be confusing. Is, for example, the value 1.06E-313 not beyond the precision of calculations? Why can't the value 0.00E+00 just be written as 0.0?
5. Some tables (4 and 5) and figures (7 and 8) may be difficult to read due to the size of the text.
Author Response
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Thanks a lot for the invaluable comments and suggestions, these suggestions are very valuable for our paper, and we carefully revise the manuscript according to these suggestions.
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Open Review
(x) I would not like to sign my review report
( ) I would like to sign my review report
English language and style
( ) English very difficult to understand/incomprehensible
( ) Extensive editing of English language and style required
( ) Moderate English changes required
( ) English language and style are fine/minor spell check required
(x) I don't feel qualified to judge about the English language and style
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Yes |
Can be improved |
Must be improved |
Not applicable |
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Does the introduction provide sufficient background and include all relevant references? |
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Are all the cited references relevant to the research? |
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Is the research design appropriate? |
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Are the methods adequately described? |
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Are the results clearly presented? |
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Are the conclusions supported by the results? |
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Comments and Suggestions for Authors
- The extensive initial fragment is a general introduction, but it contains too little information about the research problem and the scientific problem solved. The abstract should be reedited and supplemented as it does not fully reflect the substantive content of the publication.
Response:
As suggested, we have carefully revised the abstract of the revised manuscript.
- Figure 4 provides little cognitive information. Perhaps it can be presented in a different form?
Response:
Figure 4 is to visualize the effect of different initial values on the chaotic mapping strategy, where improperly initial values can lead to the appearance of immobile points, when c(0) =0.25 or 0.75.
- Figure 6. How are the indices "new" and "i+new" determined for the quantities F and X? Is fbest the same as Fbest?
Response:
Where “i” denotes the i-th individual, the fitness value F and position X of each individual will change with the number of iterations “t” of the transformation. The "i+new" denote the single individual, and the "new" denotes the whole individuals after one iteration. fbest is the same as Fbest, we have modified the Figure 8 of the revised manuscript.
- Table 4 and Table 5. The precision of the listed parameter values may be confusing. Is, for example, the value 1.06E-313 not beyond the precision of calculations? Why can't the value 0.00E+00 just be written as 0.0?
Response:
The reason for this is for uniformity of data formatting and ease of reading. Finally, the formatting will be modified according to the editor's requirements.
- Some tables (4 and 5) and figures (7 and 8) may be difficult to read due to the size of the text.
Response:
As suggested, we have resized the size of tables (4 and 5) and figures (7 and 8) of the revised manuscript.
Reviewer 4 Report
The paper presents a hybrid method based on the particle swarm optimization and butterfly optimization algorithms
In my opinion, there are several major issues that need to be addressed:
1) The computational comparison with the other methods is seriously flawed. The authors compare their proposed methodology with methods that cannot be considered as "state-of-the-art".
As was discussed in [1] and [2], this practice should be abolished. New methods should be compared against the state-of-the-art, for instance jSO, EBO-CMAR (as was done in [3]), or SHADE variants (as was done in [2]).
2) The benchmark set used for the comparison is also flawed (as many of the functions have their respective optima in the zero vector) - the authors should use some of the more advanced benchmark sets (such as the ones from the CEC competitions, or the ambiguous benchmark set [4]).
3) To ensure reproducibility and to check correctness, the authors should make the code used for running the experiments available for review (by either uploading it to some public repository or submitting it alongside the manuscript).
[1] Aranha, C., et al. Metaphor-based metaheuristics, a call for action: the elephant in the room. Swarm Intelligence, volume 16, pages 1-6 (2022)
[2] LaTorre, A., et al. A prescription of methodological guidelines for comparing bio-inspired optimization algorithms. Swarm and Evolutionary Computation, volume 67, December 2021, 100973.
[3] Del Ser, J., et al. More is not Always Better: Insights from a Massive Comparison of Meta-heuristic Algorithms over Real-Parameter Optimization Problems. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1-7, doi: 10.1109/SSCI50451.2021.9660030.
[4] Kudela, J., et al. New Benchmark Functions for Single-Objective Optimization Based on a Zigzag Pattern. IEEE Access, volume 10, pages 8262-8278 (2022)
Author Response
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Thanks a lot for the invaluable comments and suggestions, these suggestions are very valuable for our paper, and we carefully revise the manuscript according to these suggestions.
***************************************************************************
Open Review
( ) I would not like to sign my review report
(x) I would like to sign my review report
English language and style
( ) English very difficult to understand/incomprehensible
( ) Extensive editing of English language and style required
(x) Moderate English changes required
( ) English language and style are fine/minor spell check required
( ) I don't feel qualified to judge about the English language and style
|
Yes |
Can be improved |
Must be improved |
Not applicable |
|
|
Does the introduction provide sufficient background and include all relevant references? |
( ) |
(x) |
( ) |
( ) |
|
Are all the cited references relevant to the research? |
( ) |
(x) |
( ) |
( ) |
|
Is the research design appropriate? |
( ) |
( ) |
(x) |
( ) |
|
Are the methods adequately described? |
( ) |
( ) |
(x) |
( ) |
|
Are the results clearly presented? |
( ) |
( ) |
(x) |
( ) |
|
Are the conclusions supported by the results? |
( ) |
( ) |
(x) |
( ) |
Comments and Suggestions for Authors
The paper presents a hybrid method based on the particle swarm optimization and butterfly optimization algorithms
In my opinion, there are several major issues that need to be addressed:
1) The computational comparison with the other methods is seriously flawed. The authors compare their proposed methodology with methods that cannot be considered as "state-of-the-art".
As was discussed in [1] and [2], this practice should be abolished. New methods should be compared against the state-of-the-art, for instance jSO, EBO-CMAR (as was done in [3]), or SHADE variants (as was done in [2]).
[1] Aranha, C., et al. Metaphor-based metaheuristics, a call for action: the elephant in the room. Swarm Intelligence, volume 16, pages 1-6 (2022)
[2] LaTorre, A., et al. A prescription of methodological guidelines for comparing bio-inspired optimization algorithms. Swarm and Evolutionary Computation, volume 67, December 2021, 100973.
[3] Del Ser, J., et al. More is not Always Better: Insights from a Massive Comparison of Meta-heuristic Algorithms over Real-Parameter Optimization Problems. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1-7, doi: 10.1109/SSCI50451.2021.9660030.
Response:
As suggested, we have read the literature [1-3] presented by the reviewers, all three papers are review papers, and the state-of-the-art, for instance jSO, EBO-CMAR (as was done in [3]), or SHADE variants considered are all 2017 [4, 5] or the improved DE algorithms proposed in 2013 [6]. According to No free Lunch theory [7], there is no universality of any algorithm, and we essentially compare more reasonably with similar algorithms, and compare with the original algorithm to highlight the effectiveness and reasonableness of the improved algorithm. We have added references to the literature [1-3] of the revised manuscript.
[4] Brest J, Maučec M S, Bošković B. Single objective real-parameter optimization: Algorithm jSO. 2017 IEEE congress on evolutionary computation (CEC). IEEE, 2017: 1311-1318.
[5] Kumar A, Misra R K, Singh D. Improving the local search capability of effective butterfly optimizer using covariance matrix adapted retreat phase. 2017 IEEE congress on evolutionary computation (CEC). IEEE, 2017: 1835-1842.
[6] Tanabe R, Fukunaga A. Success-history based parameter adaptation for differential evolution. 2013 IEEE congress on evolutionary computation. IEEE, 2013: 71-78.
[7] Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1), 67-82.
2) The benchmark set used for the comparison is also flawed (as many of the functions have their respective optima in the zero vector) - the authors should use some of the more advanced benchmark sets (such as the ones from the CEC competitions, or the ambiguous benchmark set [4]).
[4] Kudela, J., et al. New Benchmark Functions for Single-Objective Optimization Based on a Zigzag Pattern. IEEE Access, volume 10, pages 8262-8278 (2022)
Response:
As suggested, the literature [4] is published in IEEE access, and the paper has 6 citations of web of science (including 4 self-cited papers by Kudela, J.), we believe that the so-called "advanced benchmark sets" are not recognized by researchers in the field at present. We have added references to the literature [8] of the revised manuscript.
[8] Kudela, J., et al. New Benchmark Functions for Single-Objective Optimization Based on a Zigzag Pattern. IEEE Access, volume 10, pages 8262-8278 (2022)
3) To ensure reproducibility and to check correctness, the authors should make the code used for running the experiments available for review (by either uploading it to some public repository or submitting it alongside the manuscript).
Response:
As suggested, if our paper is accepted and published by your journal, the code of the paper will be made public, such as Git-hub, MathWorks, etc.
Round 2
Reviewer 4 Report
I am satisfied with the responses.
