Moth Search: Variants, Hybrids, and Applications

Juan Li, Yuan-Hua Yang, Qing An, Hong Lei, Qian Deng and Gai-Ge Wang ()
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Juan Li: School of Information Engineering, Wuhan Business University, Wuhan 430056, China
Yuan-Hua Yang: School of Computer and Information Engineering, Hubei Normal University, Huangshi 435002, China
Qing An: School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, China
Hong Lei: School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, China
Qian Deng: School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, China
Gai-Ge Wang: Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China

Mathematics, 2022, vol. 10, issue 21, 1-19

Abstract: Moth search (MS) is a nature-inspired metaheuristic optimization algorithm based on the most representative characteristics of moths, Lévy flights and phototaxis. Phototaxis signifies a movement which organism towards or away from a source of light, which is the representative features for moths. The best moth individual is seen as the light source in Moth search. The moths that have a smaller distance from the best one will fly around the best individual by Lévy flights. For reasons of phototaxis, the moths, far from the fittest one, will fly towards the best one with a big step. These two features, Lévy flights and phototaxis, correspond to the processes of exploitation and exploration for metaheuristic optimization. The superiority of the moth search has been demonstrated in many benchmark problems and various application areas. A comprehensive survey of the moth search was conducted in this paper, which included the three sections: statistical research studies about moth search, different variants of moth search, and engineering optimization/applications. The future insights and development direction in the area of moth search are also discussed.

Keywords: moth search; optimization; metaheuristic; Lévy flights (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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