Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization
Abstract
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.
Cite
@article{arxiv.2207.14667,
title = {Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization},
author = {Zuyan Chen and Adam Francis and Shuai Li and Bolin Liao and Dunhui Xiao},
journal= {arXiv preprint arXiv:2207.14667},
year = {2022}
}
Comments
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