Improved Genetic Algorithm for Solving Green Path Models of Concrete Trucks

Round 1

Reviewer 1 Report

The study is interesting but some major corrections are required.

1- What is the motivation of the study and its contribution to the literature? It should be given clearly in the introduction.

2- How have other researchers in the literature solved your problem? With optimization methods or other methods. Include them in the article as a separate section. You can also present the pros and cons of existing studies in a table.

3- Why was GA used? Why not other metaheuristics? It will also be important that you refer to their original meta-heuristic algorithms in recent years. For example, add the studies below to your sources.

4. The results of your study should be compared with different methods.For example:

https://doi.org/10.1016/j.jksuci.2023.101655

https://doi.org/10.1007/s00366-022-01604-x

Moderate editing of English language required

Author Response

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Reviewer 2 Report

Please see attached file.

 

Comments for author File: Comments.pdf

Author Response

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Reviewer 3 Report

I have few concerns related to the manuscript and the detail is given as follows:

Q1. The contribution points of the research should be highlighted in bullets form in end of the introduction part.

Q2. Some of variables used in equations are not proper define, so elaborated more in details.

Q3. Most of the cited references are out of dated, it is recommended to put more studies (from 2022 and 2023) in the literature part.

Q4. It should be better to apply the standard functions on the developed IGA and compare the results in terms of convergence response and boxplot, if possible.  

Q5. Please include the time response of both GA and IGA for converge the solution in the paper.

Q6. The details of about the comparison of IGA with GA, it is recommended to replace points mentioned on lines 372 to 380, the suggested three points to include in the paper.

1.     Compared with the standard GA, the IGA has a stronger convergence ability to generate the high-quality individuals in the early stage of the algorithm.

2.     The IGA converged the solution in 180 iterations and having the 23.01% faster convergence than the standard GA.

3.     Compared with experiment 3, the performance of improved genetic algorithm under optimal horizontal combination is improved

Q7.  It is suggested the main inferences should be given in terms of bullet points in the conclusion section, the overall achievement should also explain in terms of % in the conclusion part too.

The english quality is good. 

Author Response

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Round 2

Reviewer 1 Report

The requested revisions were made as expected.