Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization

Autor:	Chen, Zuyan, Francis, Adam, Li, Shuai, Liao, Bolin, Xiao, Dunhui
Rok vydání:	2022
Předmět:	Computer Science - Neural and Evolutionary Computing 68T05: Evolutionary algorithms genetic algorithms (computational aspects) see also 68T20 and 90C59
Druh dokumentu:	Working Paper
Popis:	A novel meta-heuristic algorithm, Egret Swarm Optimization Algorithm (ESOA), is proposed in this paper, which is inspired by two egret species' (Great Egret and Snowy Egret) hunting behavior. ESOA consists of three primary components: Sit-And-Wait Strategy, Aggressive Strategy as well as Discriminant Conditions. The performance of ESOA on 36 benchmark functions as well as 2 engineering problems are compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Harris Hawks Optimization (HHO). The result proves the superior effectiveness and robustness of ESOA. The source code used in this work can be retrieved from https://github.com/Knightsll/Egret_Swarm_Optimization_Algorithm; https://ww2.mathworks.cn/matlabcentral/fileexchange/115595-egret-swarm-optimization-algorithm-esoa. Comment: 10 pages, 5 figures, 6 tables. Source code used for this work is available online: see https://github.com/Knightsll/Egret_Swarm_Optimization_Algorithm and https://ww2.mathworks.cn/matlabcentral/fileexchange/115595-egret-swarm-optimization-algorithm-esoa. This paper has been submitted to MDPI mathematics
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2207.14667 Zobrazit plný text záznamu View this record from Arxiv