Discriminating and Clustering Ordered Permutations Using Artificial Neural Networks: A Potential Application in ANN-Guided Genetic Algorithms
Round 1
Reviewer 1 Report
1. The key issue with the paper is providing some kind of comparison with other methods in the literature, based on publicly available benchmark instances. The current experimentations seems to focus on different “variations” of the proposed strategy which, I believe, would not be enough to prove the paper contribution.
2. The entire paper could be shortened, polished and focused on the contribution of the study; there are too many formulations (e.g. long FSP formulation) and discussions on various (disjoint) concepts.
3. Literature review could be improved in terms of latest research on multiple, high performing methods addressing multiple permutation problems, which is the focus of this study. Some of the highly relevant research on permutation problems can be found below as a starting point:
a. Bierwirth, C., Mattfeld, D.C., Kopfer, H. (1996). On permutation representations for scheduling problems. In: Voigt, HM., Ebeling, W., Rechenberg, I., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN IV. PPSN 1996. Lecture Notes in Computer Science, vol 1141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61723-X_995
b. Amirghasemi, M. An effective parallel evolutionary metaheuristic with its application to three optimization problems. Appl Intell (2022). https://doi.org/10.1007/s10489-022-03599-w
c. Karimi-Mamaghan, M., Mohammadi, M., Pasdeloup, B., & Meyer, P. (2022). Learning to select operators in meta-heuristics: An integration of Q-learning into the iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research.
4. Several figures/tables could be removed for conciseness and a clearer understanding of the paper contribution. For instance, Table 6 is very confusing and could be replaced with a simple pseudocode or flowchart.
Author Response
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Author Response File: 
Author Response.pdf
Reviewer 2 Report
The authors present a technique for clustering and discriminating ordered permutations with a potential application in developing neural network-guided metaheuristics . For this purpose, they developed two different techniques to convert ordered permutations to binary-vectors and considered Adaptive Resonate Theory (ART) neural networks for clustering the resulting binary vectors. The proposed binary conversion techniques and two neural networks (ART-1 and Improved ART-1) are examined under various performance indicators. Moreover they give some illustrative examples.
The paper is well written and the language is fluent. It includes a comprehensive literature review and the results are interesting and the proofs are given in detail. I suggest you to check some punctuation mistakes.
Author Response
Please find the attached PDF file for our response.
Author Response File: Author Response.pdf
Reviewer 3 Report
The paper presents a technique for clustering and discriminating ordered permutations with a potential application in developing neural network-guided metaheuristics in solving those classes of problems. However, there are shortcomings in this paper.
1. In abstract, the author should briefly describe his work: a technique for clustering and discriminating ordered permutations and the application; besides, the author should provide data to support your work.
2. In all equation, variables should be italic, please check and revise the equations throughout, such as equation (1).
3. Section 3 mainly shows the proposed permutation to binary conversion methods; however, the methods are not clear and 2 or 3 points are suggested to describe it.
4. The format of references is problematic and should be consistent. For example, reference [17] lacks article ID.
5. English language and style are fine/minor spell check required, for example, in Fig.11(c), (d), (e) and (f), there is a problem with the subtitle.
Author Response
Please find the attached PDF file for our response.
Author Response File: Author Response.pdf
Round 2
Reviewer 3 Report
Thank the authors for their efforts. The authors have adequately addressed all my concerns in the review, and did a good job to revise and improve the paper. The paper now is suitable for publication in Applied Sciences in its current form.
