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Mayfly optimization algorithm: a review.

Authors :
Bogar, Mohit N.
Shirodkar, Ishwar D.
Kulkarni, Omkar
Jawade, Samidha
Kakandikar, Ganesh
Source :
Journal of Mechatronic & Artificial Intelligence in Engineering; Jun2024, Vol. 5 Issue 1, p17-30, 14p
Publication Year :
2024

Abstract

This paper gives a review on the bio-inspired optimization methodology known as mayfly (MA) algorithm in order to resolve issues in optimization techniques. It is a newly formed meta-heuristic optimization algorithm that focuses on the movements of masculine and feminine mayflies. It is encouraged from flying behaviour also the methods of mating in mayflies. With the help of a realistic-world separate flow planning issue along with the coupling behaviour in numerous objective optimizations, the performance of the mayfly algorithm (MA) is well evaluated. Some of the implementations of this algorithm are discussed in this paper: Bearing fault diagnosis based on the mayfly algorithm, optimizing the performance of PEMFC, Covid diagnosis, wind speed optimization, improving the scheduling of solar wind speed using mayfly optimization, detecting fault in the wind turbine gearboxes, patterning in the array antennas with the help of optimization and so on. One of the main advantages of the MA is that it combines the other optimization algorithms namely swarm optimization (PSO) with the evolutionary optimizations (GA). The motion of the mayflies that resemble nuptial dance model along with the arbitrary flight helps in the improvement of the stability within the exploration and exploitation methods. In addition, allows escape from the community peak. All the above work reviewed shows promising results from the algorithm. More work can be carried out using this algorithm in future. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
26691116
Volume :
5
Issue :
1
Database :
Complementary Index
Journal :
Journal of Mechatronic & Artificial Intelligence in Engineering
Publication Type :
Academic Journal
Accession number :
178203914
Full Text :
https://doi.org/10.21595/jmai.2024.23909