Improved Snake Optimizer Using Sobol Sequential Nonlinear Factors and Different Learning Strategies and Its Applications
Round 1
Reviewer 1 Report
Comments and Suggestions for Authors
Title: Nonlinear Sobol Sequential Snake Optimizer with Learning 2 Strategy and Its Applications
The paper can be accepted after the major revision as
1) The keywords can be written more instead of three.
2) Is this scheme always finds the global optimal solutions?
3) When the global optimal solutions are obtained then any local search technique is used to get the results?
4) Is this hybridization of the schemes?
5) Inverse tangent properties always effectively regulate the algorithm's exploration ?
6) What is the stopping criteria of the scheme?
7) Improve the conclusions
Author Response
Thank you very much for your time involved in reviewing the manuscript. On behalf of all the contributing authors, I would like to express our sincere appreciations of your constructive comments concerning our article entitled “Nonlinear Sobol Sequential Snake Optimizer with Learning Strategy and Its Applications” (Manuscript No.: mathematics-3000755). These comments are all valuable and helpful for improving our article. According to your comments, we have made extensive modifications to our manuscript to make our results convincing. Point-by-point responses to the nice reviewer are listed below document.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for Authors
Define all acronyms at the first time of use.
Numbers less than 10 must be written in word form.
Define multimodal.
What means and what are the criteria to define c1. Explain widely.
What means and what are the criteria to define c2. Explain widely.
What means and what are the criteria to define c3. Explain widely.
Fix typo errors.
In some places the paper is repetitive.
Define all variables, constants and symbols used.
What is the advantage of the generation a pattern with the random Sobol numbers. Explain widely.
What are the criteria to select the initial parameters. Explain.
Explain widely, fix and enhance fig. 3.
Revise Eq 35 and explain widely.
Enhance Fig. 4.
How to match the parameter information of different algorithms?
What is the point of comparison between them?
Explain dimension 30D.
The quantity of function evaluation looks so big in comparison with the population and the number of runs. Explain how are selected these values.
Tables 2 and 4 does not apport nothing to the paper add information about it and the necessity of them in the paper. What is their function and what is the use in the paper.
What means the change of value on table 3. Explain.
The results shown in fig.7 show a big number of iterations and in the paper are not mentioned the iterations. The iterations are the same of times? Explain.
If the purpose is the optimization the variation on the percentage tends to be reduced and in the graphs of fig. 6 persists a variation and in some point converge suddenly. Explain this behavior. There does not show a classic behavior of the systems with overshoot and amping that reduces across the time after stabilization.
The first paragraph is not an analysis only is a resume of the tables this section needs to be rewritten with a discussion of the results obtained.
Explain what means the values of mean rank in fig. 7.
Beyond the number of evaluations of the function, what are the computational times?
There are several tables with a lot of results, but the explanations and discussion are missing.
On the tested problems does not appear the goal to see the accuracy of the algorithm tested.
Comments on the Quality of English Language
Exists typo errors.
Author Response
Thank you very much for your time involved in reviewing the manuscript. On behalf of all the contributing authors, I would like to express our sincere appreciations of your constructive comments concerning our article entitled “Nonlinear Sobol Sequential Snake Optimizer with Learning Strategy and Its Applications” (Manuscript No.: mathematics-3000755). These comments are all valuable and helpful for improving our article. According to your comments, we have made extensive modifications to our manuscript to make our results convincing. Point-by-point responses to the nice reviewer are listed below document.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for Authors
Accept
Author Response
Dear Reviewer:
Thank you very much for your time involved in reviewing the manuscript. On behalf of all the contributing authors, I would like to express our sincere appreciations of your constructive comments concerning our article entitled “Nonlinear Sobol Sequential Snake Optimizer with Learning Strategy and Its Applications” (Manuscript No.: mathematics-3000755). These comments are all valuable and helpful for improving our article.
Many thanks to the reviewers for agreeing to the acceptance of our manuscript. We wish the reviewers all the best in their work.
Author Response File: 
Author Response.docx
Reviewer 2 Report
Comments and Suggestions for Authors
Several comments are nod adressed or not addressed adequately.
e.g. acronyms are not defined at the fist time of use.
The comment "Numbers less than 10 must be written in word form" was not addressed in the correct section.
The comments 4-6 were only mention that are taken from literature without more explanation. why not test other values?
Comment 11, again only take the parameters from litearturewithout additional tests and without jutification also, the parameters are not mentioned.
Comment 12. was not addressed. Enhace the figure with enlargement.
the explanation on comment 15 is not convincent due the parameters for the different algorithms are different and the conditions for everyone are different to then, how to addapt the proposal to every algorithm to make the test and have a comparison. This situation also affects the mentioned in comment 16.
In comment 17 can add a depiction.
The population still appear too small for an optimization algorithm. exists a justification for this fact. Explain.
Author Response
Dear Reviewer:
Thank you very much for your time involved in reviewing the manuscript. On behalf of all the contributing authors, I would like to express our sincere appreciations of your constructive comments concerning our article entitled “Nonlinear Sobol Sequential Snake Optimizer with Learning Strategy and Its Applications” (Manuscript No.: mathematics-3000755). Also, apologies to the reviewers for the shortcomings in the last round of improvements. These comments are all valuable and helpful for improving our article. According to your comments, we have made extensive modifications to our manuscript to make our results convincing. Point-by-point responses to the nice reviewer are listed below this letter.
Author Response File: Author Response.docx
Round 3
Reviewer 2 Report
Comments and Suggestions for Authors
In comment 3. Why establish two parametrsa as constant. Recommend try at leas two combinations with other parameters to see the behavior of the system.
In comment 4. The figures must be added in bigger sizes to see if exists variations that can not be apreciated in the actual size.
In comment 6. why not make experiments with a population with less than 30 individuals.
Author Response
Dear Reviewer:
Thank you very much for your time involved in reviewing the manuscript. On behalf of all the contributing authors, I would like to express our sincere appreciations of your constructive comments concerning our article entitled “Nonlinear Sobol Sequential Snake Optimizer with Learning Strategy and Its Applications” (Manuscript No.: mathematics-3000755). Also, apologies to the reviewers for the shortcomings in the last round of improvements. These comments are all valuable and helpful for improving our article. According to your comments, we have made extensive modifications to our manuscript to make our results convincing. Point-by-point responses to the nice reviewer are listed below this letter.
Author Response File: Author Response.docx
