Distribution Network Regionalized Fault Location Based on an Improved Manta Ray Foraging Optimization Algorithm

Round 1

Reviewer 1 Report

The comments are in the attached file.

Comments for author File: Comments.pdf

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

This paper presents distribution network regionalized fault location based on improved Manta Ray foraging optimization algorithm.  Upon review of the paper, major concerns are listed as follow:

1-            What is the major contribution of the paper? Only using based on improved Manta Ray foraging optimization algorithm is the novelty of the proposed method? More and clear description is necessary. Also please revise the abstract.

2-            A comparison study (other approaches of fault location) to show the effectiveness of the proposed method is necessary.

  

 

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

The authors apply the IMRFO algorithm to fault location. The paper is partially supported by theory and includes illustrative validations. Comments:
1. Please edit the paper carefully such that to respect the instructions for authors. A homogeneous style is desired.
2. The abstract is very long and the novelty is missing. Some words about the results should be added.
3. You should present the contributions with respect to your past papers that should be cited.
4. The optimization problem is not defined. You are specifying a fitness function.
5. As mentioned in the comment 3, the definitions of the optimization problems must be treated with attention. The authors are advised to include the following classical amd fresh optimization algorithms: Vehicles circuits optimization by combining GPS / GSM information with metaheuristic algorithms (ROMJIST 2020), GWO-based optimal tuning of type-1 and type-2 fuzzy controllers for electromagnetic actuated clutch systems (IFAC-PapersOnLine 2021), Gravitational search algorithm-based tuning of fuzzy control systems with a reduced parametric sensitivity (in Soft Computing in Industrial Applications 2011).
6. The connection between the optimization algorithms and the optimization problem is also not pointed out.
7. The connection between the applications (Section 4) and the previous theory is not clear enough. More details are necessary for improved transparency.
8. You should save the code of programs and datasets, and cite the link to them in the paper. This is useful for validation, and helps the above comment. The importance of this comment is related to the fact that similar optimization algorithms are reported in the literature, they report excellent results but cannot be tested.
9. You should specify which are the parameters of the optimization algorithms, which of them should be selected by the user and which of them are random.
10. I am not sure if the comparison is correct because all algorithms used in the comparison including yours depend on parameters. Other parameters lead to other results.

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

The authors revised the manuscript. I think current version of the paper is acceptable.

Author Response

Once again,thank you very much for your comments and suggestions.

Reviewer 3 Report

I appreciate the revisions made but the following comments were not solved:

1. The optimization problem is not defined.

2. In this context, it is not shown what does the optimization algorithm actually solve.

3. Although the validation is fully transparent, the comparison is not.

4. In the context of the comment 3, the motivation of using this algorithm is not clear either.

 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf