Multi-Population Differential Evolution Algorithm with Uniform Local Search

Round 1

Reviewer 1 Report

1.In their manuscript, the author self-cites far too frequently while ignoring several variations of DE, such as DE with different local searches, DE with reincarnation processes, etc. 2. What is the suggested DE's current position in relation to other cutting-edge metaheuristics that have been proposed in the literature thus far, such as GA, Tabu search, SA, AMIS, etc.? This article only compares their algorithm to other algorithms that operate in the same way. 3. Why is the only criterion for stopping algorithm testing the number of solutions generated? Given that the complexity of the studied methods differs, it would not be fair to compare them without also using comparable computing time. 4. Additionally, I'm curious as to why the proposed DE is superior to previously DE variants. It would be unfair to compare only search behavior.

Author Response

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Reviewer 2 Report

This manuscript  is well written and contains detailed discussion on the presented subject. In consequence, I have not suggestions to improve this article.

In this manuscript, authors have proposed a novel multiple island Differential Evolution algorithms, a multi-population Differential Evolution algorithm with uniform local search (MUDE), which has improved population diversity through migration with the island. In the course of evolution, the whole population was randomly split into many sub-islands, and each sub-island has carried out different strategies according to the evolution ratio. To advance DE diversity, individuals of the island were migrated through the soft-island model. Uniform local search was used to improve population exploitation. The experimental results have shown that MUDE is effective and efficient by comparing it with four DE variants on a set of 25 functions of CEC 2005.

Given that the proposed method is a heuristic and the processing time is high it should be clarified the advantages of using this heuristic method instead other methods.

Author Response

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Author Response File: Author Response.docx

Reviewer 3 Report

The manuscript is well-written. However, the following comments need to be addressed first:

1. The abstract section needs to be re-written. The problem statement, results and benefits of the developed model should be added to it.
2. The introduction section needs to be separated from the literature review section. The problem statement and research objectives should be included in the introduction section.
3. More recent studies need to be added in the literature review section:

a)     Vafashoar, R., & Meybodi, M. R. (2020). A multi-population differential evolution algorithm based on cellular learning automata and evolutionary context information for optimization in dynamic environments. Applied Soft Computing, 88, 106009.

b)     Al-Sakkaf, A., Mohammed Abdelkader, E., Mahmoud, S., & Bagchi, A. (2021). Studying Energy Performance and Thermal Comfort Conditions in Heritage Buildings: A Case Study of Murabba Palace. Sustainability, 13(21), 12250.

4. It is not clear what are the distinctive features of the proposed optimal field sampling method.
5. Limitations of previous studies should be added to the manuscript.
6. A research framework figure and section should be presented to show the steps of the developed model.
7. Model validation and results need to be collected in a separate section towards the end of the manuscript.
8. The conclusion section should be strengthened. More insight into the result should be added. Also, the limitations of the present research study should be added at the end of the conclusion section.

Author Response

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Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

good luck

Author Response

Thank you for your decision and constructive comments on my manuscript.