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Thank you for carefully revising your manuscript and addressing all of the reviewers concerns.
[# PeerJ Staff Note - this decision was reviewed and approved by Daniel Katz, a 'PeerJ Computer Science' Section Editor covering this Section #]
- No further comments. The paper can be accepted for publication.
- No further comments. The paper can be accepted for publication.
- No further comments. The paper can be accepted for publication.
This is a well written and well researched paper. The reviewers have given only small amounts of feedback and only minor changes to the manuscript are required. When preparing the revised manuscript please pay particular attention to improving the legibility of figures.
Reviewer 3 has given a number of useful suggestions, the authors should carefully consider running the additional test bed functions and adding the requested comparisons even though this will require additional work it will considerably strengthen the resultant manuscript.
In the study, the authors proposed a PSO variant, in which the inertia weight and the learning model are improved.
The experimental results verify the improved PSO can offer promising performance.
The new introduced strategies play positive performance on convergence speed and solutions' accuracy.
The performance or the characteristics of new proposed strategies should be analyzed deeply.
- Write an opening paragraph for Section 2.
- Change the title of Section 2 to "Related Work".
- The adverb "where" after an equation should be written with small letters.
- The limitations of the proposed approach should be mentioned in the conclusion section.
- Change the title of the last section to "Conclusions and Future Works".
- The future work directions at the end of the conclusion should be presented in a better way.
- Write an opening paragraph for Section 4.
- The computational complexity of the proposed algorithm should be compared with the original PSO algorithm.
- The proposed algorithm should be compared with additional efficient numerical optimization algorithms: Distributed GWO (DGWO), and island cuckoo search (iCSPM2).
- The convergence charts should be discussed with more details.
- The charts in the experimental section should be presented in a better way. Perhaps make them larger and display fewer charts each row.
The authors have proposed the variants of Particle Swarm Optimization to solve various CEC test bed systems.
the paper is nicely written and well organized.
The proposed method should be compared with the latest state of the art WCCI(CEC)/GECCO 2022 test bed functions.
and it should be statistically compared with CEC 2022 algorithms.
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Please improve Figure 3. It is very tiny.
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