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Peer-Review Record

Enhanced Teaching–Learning-Based Optimization Algorithm for the Mobile Robot Path Planning Problem

Appl. Sci. 2023, 13(4), 2291;
by Shichang Lu, Danyang Liu *, Dan Li and Xulun Shao
Reviewer 1: Anonymous
Reviewer 2:
Appl. Sci. 2023, 13(4), 2291;
Submission received: 28 December 2022 / Revised: 7 February 2023 / Accepted: 8 February 2023 / Published: 10 February 2023

Round 1

Reviewer 1 Report

This work presents a metaheuristic approach combining oppositional learning, differential evolution, and teaching-learning algorithm for solving the mobile robot path planning. The algorithm is called the enhanced teaching-learning algorithm (ETLBO). The paper is interesting and relevant and can be accepted after taking care of the following comments as

1- The ETLBO was benchmarked using only five benchmark functions which is not enough. Please further improve the benchmarking by adding more relevant benchmark functions.  

2- The parametric studies only considered the STD and mean, the benchmarking should be improved by using the min, max, and median. This can give a better picture of the performance of the (ETLBO).

3- The authors have compared the ETLBO with only a few TLBO versions, which is not enough. Please add a comparison with other state-of-the-art algorithms.

4- The ETLBO should be ranked using the Friedmann rank test, which makes it clearer to judge the performance of the algorithm. 

5- Non-parametric Wilcoxon test is recommended to be done. It can be simply done using one Matlab command. 

6- The reference list should be extended to have at least 30 references.

7- In section 4.3.1, the authors used oppositional learning to initialize the ETLBO, the authors should take care of recent articles implementing this method to appreciate relevant research to your paper. For example, what is the difference between this oppositional method and the following application of oppositional learning:

a- LARO: Opposition-Based Learning Boosted Artificial Rabbits-Inspired Optimization Algorithm with Lévy Flight

b- The new self-adaptive quasi-oppositional stochastic fractal search

c-  Oppositional based Pigeon Inspired Optimizer

d-the Novel Oppositional Unified Particle Swarm Gradient-Based Optimizer

Please carefully discuss this in the introduction section or in section 4.3.1 


8-A discussion section is recommended to be added to discuss all the results. It must include the major limitations, difficulties, achievements, and recommendations.

9- The conclusion should be more carefully rewritten, summarizing what has been learned and why it is interesting and useful. Here it is recommended to separate the discussion and the conclusion section.


Author Response


Author Response File: Author Response.docx

Reviewer 2 Report

Please see the attached file for detail comments. 


Comments for author File: Comments.docx

Author Response

Please see the attachment

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The paper can be accepted for publication.

Reviewer 2 Report

The authors have addressed all the comments and the paper looks good now. Therefore, I have no more comments. 


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