Adaptive Cruise Predictive Control Based on Variable Compass Operator Pigeon-Inspired Optimization
Round 1
Reviewer 1 Report
In my opinion, the paper contains a valuable conclusion that the PIO (Pigeon-Inspired Optimization) leads to the stable state faster than the PSO (Particle Swarm Optimization). However, the paper needs many refinements before eventual publishing. These are my comments and suggestions for improving the text:
——Abstract/Introduction: Some more detailed assessment about the novelty and originality of the paper should be included in both Abstract and Introduction.
——All the inline formulae: they are ABOVE the text, you must set the equation editor correctly. (Starting the line 43, it is then everywhere...)
——The symbols around the line 55 are insufficiently explained, e.g., the terms "system gain" (how is defined?), "time constant" (of which?).
——Again, I think, the "swarm" of the equations in the lines 68 through 78 are not sufficiently commented.
——The dots in (5) are also "nonstandardized".
——The in-line equation in the line 114 is exceptionally big (for the purpose to be in an in-line form). (And some of the subscripts are extremely small.) Please rearrange this!
——The Figures 1 and 2 are of a very bad quality (especially when printed). They are too small bitmaps. The figures must be redone (at the best, in the vector form instead of the bitmap one) to be readable...
——References: In my opinion, the referencing is not sufficient as there are many interesting works concerning PSO or PIO (or both). Therefore, I suggest more citations to the multi-objective optimization (for both direct and metaheuristic methods), e.g., the following:
————FAKHFAKH, M., SALLEM, A., BOUGHARIOU, M., BENNOUR, S., BRADAI, E., GADDOUR, E., LOULO,U M. Analogue circuit optimization through a hybrid approach. Intelligent Computational Optimization in Engineering, Studies in Computational Intelligence. Berlin (Germany): Springer, 2011, vol. 366, p. 297–327.
————SHORBAGY, M., MOUSA, A. A. A., FATHI, W. Hybrid Particle Swarm Algorithm for Multiobjective Optimization: Integrating Particle Swarm Optimization with Genetic Algorithms for Multiobjective Optimization. Saarbrücken (Germany): LAP (LambertAcademic Publishing), 2011.
————DOBES, J., MICHAL, J., BIOLKOVA, V. Multiobjective optimization for electronic circuit design in time and frequency domains. Radioengineering, 2013, vol. 22, no. 1, p. 136–152.
————HIRANO, H., YOSHIKAWA, T. A study on two-step search using global-best in PSO for multi-objective optimization problems. In Proceedings of the 6th International Conference on Soft Computing and Intelligent Systems/13th International Symposium on Advanced Intelligent Systems (SCIS/ISIS). Kobe (Japan), 2012.
————LI, L., XU, S., NIE, H., MAO, Y., YU, S. Collaborative Target Search Algorithm for UAV Based on Chaotic Disturbance Pigeon-Inspired Optimization. Applied Sciences. 2021 — mdpi.com, 11(16), 7358; https://doi.org/10.3390/app11167358
etc.
Generally, I recommend the paper for publishing after necessary improvements. (Which surely must be made!)
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The following points can improve this manuscript
- The motivation behind this study is lacking. The abstract should be a summary of the whole paper, but that is not the case here. How is PIO better that PSO? While PIO is detailed and defined, PSO appears only in the comparison section (last line of the abstract)
- Similar to the abstract, PSO is not well documented in the introduction neither in the methodology.
- The practical application of the findings is lacking in the conclusion section. How is this study useful to researchers and practitioners?
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
The authors responded to my comments and recommendations in the following way:
——The requested assessment of the paper novelty has been included in Abstract and Introduction in a comprehensive way. Moreover, a number of new citations have been added as well. (In the 1st version, citing were insufficient, in my opinion).
——The wrong method of the in-line equation edition was corrected. (Although not ideal, it is sufficient now.)
——The missing symbols around the line 55 were explained, now sufficiently.
——The equations describing the swarm optimization were more commented, now they are clearer.
——The wrong dots in (5) were repaired, and the in-line equation in the line 114 was modified as well.
——The original poor figures were redone. Although they are not still vectorized, their quality is now sufficient, in my opinion.
——The reference list was supplemented very well.
Therefore, I recommend this paper for publishing now.
