Application of Metaheuristic Optimisation Techniques for the Optimisation of a Solid-State Circuit Breaker

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This is an excellent piece of work! It is recommended for publication pending the resolution of the following points:

1. Please explicitly list the highlights of your research at the end of the introduction to clearly emphasize its significance.

2. In the section "2. Optimisation of SSCBs," it would be beneficial to see a formally stated problem formulation, including the objective function and all constraints. Since the focus is on optimization, this should be presented in detail. Please also list the optimized variables and their allowable ranges.

3. It is good that the section describing the optimization problem demonstrates the problem's scale and the number of possible combinations. However, metaheuristic algorithms are primarily applied when the objective function is non-differentiable and/or complex. This section could be strengthened by including a graph showing the behavior of the objective function with respect to its parameters, to visually demonstrate why classical gradient-based methods are unsuitable.

4. Given that this is a multi-objective optimization problem, it is unclear why the authors did not consider dedicated methods such as NSGA-II or MOGA, which are specifically designed for such cases. A justification for the chosen approach over these established methods would be valuable.

5. A related question concerns the selection of algorithms. Why were these three specific methods chosen? As is well known, a great many metaheuristics exist. A brief rationale for this selection would be helpful for the reader.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The authors propose the application of three metaheuristics for optimizing the performance of solid-state circuit breakers (SSCBs). While the techniques are well-established, the introduction focuses solely on the algorithms without discussion previous research related to the application of optimization approaches to solving such a similar problem. The contributions and research gaps remain unclear.

Although the comparison presented in Table 1 is sound, it contains well-known information. However, these classical approaches suffer from severe limitations. Notably, PSO is prone to premature convergence and trapping around local optima in complex, multimodal problems. GA requires careful tuning of parameters and may converge slowly due to its stochastic nature. Furthermore, GWO can experience slow convergence and reduced exploration in high-dimensional search spaces. Simply applying existing algorithms to a given problem is not sufficient.

The theoretical background is weak. The optimization problem is not properly formulated in Section 2 in terms of an objective function, constraints, and decision variables, making it very hard to understand how the proposed metaheuristics are applied or evaluated within the problem framework. Parameter specifications remain ungiven throughout the manuscript. Overall, Sections 2 and 3 are loosely organized and incomplete.

As for the results, Section 4 fails to provide convincing evidence of improved performance achieved by the metaheuristics. While an experimental prototype is readily available, it remains underexplored in the analysis. The authors tested it under steady-state conditions and within a limited current range, without transient or fault scenarios that are critical to SSCB operation. Overall, the reported results lack depth, scalability analysis, and a comprehensive discussion. The manuscript resembles a technical report, with limited archival value as a scientific study.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

1. The title of the manuscript does not adequately describe the research work undertaken. The authors should replace the words “Genetic Algorithm” with “Metaheuristic Optimization Techniques”
2. If possible, the authors should expand literature review to show a more comprehensive picture of what is currently depicted in literature.
3. In the introduction, clearly state contributions of study and relevant investigation objectives
4. At the end of the introduction, provide an outline of the remainder of the manuscript.
5. The equations should be appropriately cited.
6. Figure 3 needs to be reworked-maybe show only two temperatures to allow for a clearer observation.
7. The mathematical model of MOSFET needs to be clearly described.
8. The initial conditions for the algorithms are provided but differ to one another. Why is this so? What are the implications of subjecting each algorithm to the same number of search agents and iterations? Please discuss.
9. Figure 6 should be separate into different images to demonstrate a higher resolution.
10. Provide a block diagram of pseudo-code of how the cost function is set up. Currently, this is not clear.
11. These algorithms are stochastic in nature-performance may differ between algorithm implementations. Given that the algorithms do not take a long time to run, the authors should consider running each algorithm at least twenty time, to obtain a standard deviation.
12. Why were those algorithms chosen? Recently, there have been many new metaheuristic optimization techniques developed. Studies have shown that these algorithms can outperform older algorithms. The authors implement at least one recently developed algorithm, for example, the Gorilla Troops Optimizer.
13. The results section lacks rigour. More experiments are required to draw a conclusion

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Thanks to the authors for their work. It is proposed to accept the article in present form.

Author Response

We thank the reviewer for their positive assessment of our work and for recommending acceptance in its present form. We appreciate the time taken to review the manuscript.

Reviewer 2 Report

Comments and Suggestions for Authors

The authors addressed most technical issues. As a final advice, I kindly ask them to check references. Some of them appear incorrect or miss important data e.g. [27] and [28]. Furthermore, thesis and dissertations must be removed from the reference list, e.g., [26].

Author Response

We thank the reviewer for highlighting the issues with the reference list. As suggested, we have now carefully reviewed and corrected all references. Specifically:

• The previously inaccurate or incomplete references have been updated with full bibliographic details (including the items formerly listed as [27] and [28]).
• Reference formatting has been standardised throughout for consistency.
• All thesis/dissertation sources have been removed from the reference list, as requested.

We appreciate the reviewer’s attention to detail, and these corrections have now been incorporated into the revised manuscript.