论文标题
乌格莱特群体优化算法：用于模型免费优化的进化计算方法
Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization
论文作者
论文摘要
本文提出了一种新型的元元素算法，白鹭群优化算法（ESOA），其灵感来自两种乌格莱特物种（大白如象鼻虫和雪绿色的艾格莱特）狩猎行为。 ESOA由三个主要组成部分组成：静坐战略，积极的策略以及判别条件。将ESOA在36个基准功能以及2个工程问题上的性能与粒子群优化（PSO），遗传算法（GA），差异进化（DE），灰狼优化器（GWO）和Harris Hawks优化（HHO）进行了比较。结果证明了ESOA的出色有效性和鲁棒性。可以从https://github.com/knightsll/egret_swarm_optimization_algorithm中检索此工作中使用的源代码; https://ww2.mathworks.cn/matlabcentral/fileexchange/115595-Egret-swarm-optimization-algorithm-esoa。
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.
