DM: Dehghani Method for Modifying Optimization Algorithms
, David Sotelo 4, Ali Ehsanifar 3, Om Parkash Malik 5, Josep M. Guerrero 6, Gaurav Dhiman 7 and Ricardo A. Ramirez-Mendoza 4,*Round 1
Reviewer 1 Report
In this paper, a new modification method has been presented for optimization algorithms called Dehghani method (DM). The main idea of the proposed DM is to improve and strengthen the best member of the population using the information of the population members. in DM all members of a population, even the worst one, can contribute to the development of the population.
In my opinion, some comments the authors could consider in a revised version are:
- The authors do not discuss or compare the proposed method with other methods and techniques that improve the quasi-optimal solution or that the algorithm is not trapped in a local minimum.
- There is no evaluation of the computational cost involved in applying the proposed method.
Author Response
DM: Dehghani Method for Modifying Optimization Algorithms
Ricardo A. Ramirez-Mendoza
Tecnológico de Monterrey, Monterrey NL, 64,489, Mexico
13-October-2020
Applied Sciences Editorial Office
The authors appreciate dear Editor-in-Chief, managing editor, MDPI
Assistant Editor, and the respected reviewers for the carefully consideration and useful comments on the paper. It surely improves the quality of the paper. The paper is revised according to the recommendation and comments given in the decision letter. In the following, the authors' answers and list of changes are presented according to the comments. It must be noted, these modifications are highlighted in the paper.
Best regards
Ricardo A. Ramirez-Mendoza
Email: [email protected]
Reviewers Recommendation:
Comments from dear reviewer 1:
In this paper, a new modification method has been presented for optimization algorithms called Dehghani method (DM). The main idea of the proposed DM is to improve and strengthen the best member of the population using the information of the population members. in DM all members of a population, even the worst one, can contribute to the development of the population.
The authors appreciate dear reviewer for the carefully consideration and useful comments on the paper. It surely improves the quality of the paper. Based on these valuable comments, the article has been revised. The authors hope that the revised paper will be accepted by dear reviewer.
In my opinion, some comments the authors could consider in a revised version are:
- The authors do not discuss or compare the proposed method with other methods and techniques that improve the quasi-optimal solution or that the algorithm is not trapped in a local minimum.
Response: Thank you so much to the dear reviewer for this valuable and accurate comment. The authors have used a set of 5 widely used optimization algorithms to analyze the proposed method.
These algorithms are implemented on a set of twenty-three standard objective functions.
These standard functions have been used by researchers in similar articles.
Results are presented for both the original and water-modified versions.
Convergence curve and computational time analyzes are also presented.
- There is no evaluation of the computational cost involved in applying the proposed method.
Response: Thank you so much to the dear reviewer for this valuable and accurate comment. Based on this valuable comment, the computational time per iteration and the overall time for the original and modified algorithms is added. Tables 7 to 9 has been added.
Lines 206 to 211.
Tables 7 to 9.
The authors appreciate dear reviewer for the carefully consideration and useful comments on the paper. It surely improves the quality of the paper. Based on these valuable comments, the article has been revised. The authors hope that the revised paper will be accepted by dear reviewer.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
While the paper is relevant to the journal's editorial scope and has some significant contribution, comparisons to others’ work would be beneficial for readers along with information about other measures in other domains.
The method was explained in a way that other researchers cannot replicate. It is so unclear to me that is almost impossible to understand.
I can't figure out why how this method performs so much better than the original ones in all the cases. This seems pretty odd to me. Are the experiments real?
There is no comparison about time to run your method, how much time does your optimization method take to obtain reliable results?
Finally, 18 out of 49 references (37%) come from the first author and/or other authors in this paper. Is self-citing so much really necessary?
Author Response
DM: Dehghani Method for Modifying Optimization Algorithms
Ricardo A. Ramirez-Mendoza
Tecnológico de Monterrey, Monterrey NL, 64,489, Mexico
13-October-2020
Applied Sciences Editorial Office
The authors appreciate dear Editor-in-Chief, managing editor, MDPI
Assistant Editor, and the respected reviewers for the carefully consideration and useful comments on the paper. It surely improves the quality of the paper. The paper is revised according to the recommendation and comments given in the decision letter. In the following, the authors' answers and list of changes are presented according to the comments. It must be noted, these modifications are highlighted in the paper.
Best regards
Ricardo A. Ramirez-Mendoza
Email: [email protected]
Reviewers Recommendation:
Comments from dear reviewer 2:
While the paper is relevant to the journal's editorial scope and has some significant contribution, comparisons to others’ work would be beneficial for readers along with information about other measures in other domains.
The authors appreciate dear reviewer for the carefully consideration and useful comments on the paper. It surely improves the quality of the paper. Based on these valuable comments, the article has been revised. The authors hope that the revised paper will be accepted by dear reviewer.
- The method was explained in a way that other researchers cannot replicate. It is so unclear to me that is almost impossible to understand.
Response: Thank you so much to the dear reviewer for this valuable comment. The authors have tried to explain the proposed method as simply as possible.
In order to better understand the proposed method, Algorithm 1 and Algorithm 2 are included in the text of the article.
A flowchart of the proposed method is also provided.
Based on the explanations of the dear reviewer, the authors will make available to readers the MATLAB code of the proposed method at the same time as the possible acceptance of the article. In this case, readers can easily use it.
- I can't figure out why how this method performs so much better than the original ones in all the cases. This seems pretty odd to me. Are the experiments real?
Response: Thank you so much to the dear reviewer for this valuable and accurate comment. These results are quite real. The authors confirm the accuracy of the results.
In order to be more confident for the dear reviewer, MATLAB code has been attached so that the esteemed referee can check the results.
After accepting the article, the authors will also publish MATLAB code so that readers can use the suggested method in their research.
- There is no comparison about time to run your method, how much time does your optimization method take to obtain reliable results?
Response: Thank you so much to the dear reviewer for this valuable and accurate comment. Based on this valuable comment, the computational time per iteration and the overall time for the original and modified algorithms is added. Tables 7 to 9 has been added.
Lines 206 to 211.
Tables 7 to 9.
- Finally, 18 out of 49 references (37%) come from the first author and/or other authors in this paper. Is self-citing so much really necessary?
Response: Thank you so much to the dear reviewer for his valuable and accurate comment. Although the references used belong to one of the authors, given that the main field of his research is optimization, these sources are relevant to the subject of the present article. However, if the dear reviewer finds any of the references used to be irrelevant, the authors will delete that reference.
Author Response File: Author Response.pdf
Reviewer 3 Report
In this paper, Authors propose new modification method called Dehghani Method (DM) designed to improve the performance of optimization algorithms.
The proposed modification is simple in nature, easy to understand and implement and also according to results can have profound influence w.r.t precision and convergence speed. The only thing missing in the analysis is the computational impact DM method has in the overall time required for the optimization algorithm to find quasi-optimal solution. This analysis should show computational time per iteration and the overall time required for the original and modified algorithm to achieve similar objective function value.
The English language is mostly ok, but I would suggest that authors use “Avg” instead of the “Ave” as the abbreviation of average.
Author Response
DM: Dehghani Method for Modifying Optimization Algorithms
Ricardo A. Ramirez-Mendoza
Tecnológico de Monterrey, Monterrey NL, 64,489, Mexico
13-October-2020
Applied Sciences Editorial Office
The authors appreciate dear Editor-in-Chief, managing editor, MDPI
Assistant Editor, and the respected reviewers for the carefully consideration and useful comments on the paper. It surely improves the quality of the paper. The paper is revised according to the recommendation and comments given in the decision letter. In the following, the authors' answers and list of changes are presented according to the comments. It must be noted, these modifications are highlighted in the paper.
Best regards
Ricardo A. Ramirez-Mendoza
Email: [email protected]
Reviewers Recommendation:
Comments from dear reviewer 3:
In this paper, Authors propose new modification method called Dehghani Method (DM) designed to improve the performance of optimization algorithms.
The authors appreciate dear reviewer for the carefully consideration and useful comments on the paper. It surely improves the quality of the paper. Based on these valuable comments, the article has been revised. The authors hope that the revised paper will be accepted by dear reviewer.
- The proposed modification is simple in nature, easy to understand and implement and also according to results can have profound influence w.r.t precision and convergence speed. The only thing missing in the analysis is the computational impact DM method has in the overall time required for the optimization algorithm to find quasi-optimal solution. This analysis should show computational time per iteration and the overall time required for the original and modified algorithm to achieve similar objective function value.
Response: Thank you so much to the dear reviewer for this valuable and accurate comment. Based on this valuable comment, the computational time per iteration and the overall time for the original and modified algorithms is added. Tables 7 to 9 has been added.
Lines 206 to 211.
Tables 7 to 9.
The English language is mostly ok, but I would suggest that authors use “Avg” instead of the “Ave” as the abbreviation of average.
Response: Thank you so much to the dear reviewer for this valuable and accurate comment. To address this valuable comment, authors use “Avg” instead of the “Ave” as the abbreviation of average.
Lines 179 to 190
Tables 2 to 6
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
The authors have addressed most of my comments and the paper is now publishable.
