  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.
