Differential Evolution-Boosted Sine Cosine Golden Eagle Optimizer with Lévy Flight

作     者：Gang Hu Liuxin Chen Xupeng Wang Guo Wei Gang Hu;Liuxin Chen;Xupeng Wang;Guo Wei

作者机构：Department of Applied MathematicsXi’an University of TechnologyXi’an710054People’s Republic of China School of Computer Science and EngineeringXi’an University of TechnologyXi’an710048People’s Republic of China School of Art and DesignXi’an University of TechnologyXi’an710054China Department of Mathematics and Computer ScienceUniversity of North Carolina at PembrokePembrokeNC28372USA 

出 版 物：《Journal of Bionic Engineering》 (仿生工程学报（英文版）)

年 卷 期：2022年第19卷第6期

页      面：1850-1885页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：National Natural Science Foundation of China(Grant No.51875454) 

主　　题：Golden eagle optimizer Lévy flight Sine cosine algorithm Differential evolution strategy Engineering design Bionic model 

摘      要：Golden eagle optimizer(GEO)is a recently introduced nature-inspired metaheuristic algorithm,which simulates the spiral hunting behavior of golden eagles in ***,the GEO suffers from the challenges of low diversity,slow iteration speed,and stagnation in local optimization when dealing with complicated optimization *** ameliorate these deficiencies,an improved hybrid GEO called IGEO,combined with Lévy flight,sine cosine algorithm and differential evolution(DE)strategy,is developed in this *** Lévy flight strategy is introduced into the initial stage to increase the diversity of the golden eagle population and make the initial population more abundant;meanwhile,the sine-cosine function can enhance the exploration ability of GEO and decrease the possibility of GEO falling into the local ***,the DE strategy is used in the exploration and exploitation stage to improve accuracy and convergence speed of ***,the superiority of the presented IGEO are comprehensively verified by comparing GEO and several state-of-the-art algorithms using(1)the CEC 2017 and CEC 2019 benchmark functions and(2)5 real-world engineering problems *** comparison results demonstrate that the proposed IGEO is a powerful and attractive alternative for solving engineering optimization problems.