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.
翻译：本文提出一种新型的超重力算法,即Egret Swart Swarm Apromimization Algorithm(ESOA),它受两种埃格雷特物种(大埃格雷特和雪雪色Egret)的狩猎行为启发。ESOA由三个主要组成部分组成:静坐和等待战略、侵略战略以及差异性条件。ESOA在36个基准功能和2个工程问题方面的表现与Paters Swarm Aprimization(PSO)、遗传性阿尔戈里特西姆(GA)、不同演化(DE)、灰色狼最佳化(GWO)和Harris Hawks Optim(HHO)。结果证明了ESOA的超强效力和稳健性。这项工作所使用的源代码可从https://github.com/Knightsll/Egret_Swarm_Optimination_Algorimsm;https://w2.mathworks.clabcent/plabcent/foraswarmastria/ 5595-griamalim-gres-grerestrialim-gres)。
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