Multi-Strategy Grey Wolf Optimization Algorithm for Global Optimization and Engineering Applications  

作　　者：Likai Wang Qingyang Zhang Shengxiang Yang Yongquan Dong 

机构地区：[1]School of Computer Science and Technology,Jiangsu Normal University,Xuzhou 221000,China [2]School of Computer Science and Informatics,De Montfort University,Leicester,LE19BH,Leicester,UK

出　　处：《Journal of Systems Science and Systems Engineering》2025年第2期203-230,共28页系统科学与系统工程学报(英文版)

基　　金：supported by the National Natural Science Foundation of China under Grants Nos.62006103 and 61872168;in part by the Postgraduate research and practice innovation program of Jiangsu Province under Grand No.KYCX24_3057;in part by the Postgraduate Research and Practice Innovation Program of Jiangsu Normal University under Grand Nos.2024XKT2643 and 2024XKT2642;in part by Xuzhou Basic Research Program under Grand No.KC23025;in part by the Royal Society International Exchanges Scheme IECVNSFCV211404;in part by China Scholarship Council under Grand No.202310090064.

摘　　要：The grey wolf optimizer(GWO),a population-based meta-heuristic algorithm,mimics the predatory behavior of grey wolf packs.Continuously exploring and introducing improvement mechanisms is one of the keys to drive the development and application of GWO algorithms.To overcome the premature and stagnation of GWO,the paper proposes a multiple strategy grey wolf optimization algorithm(MSGWO).Firstly,an variable weights strategy is proposed to improve convergence rate by adjusting the weights dynamically.Secondly,this paper proposes a reverse learning strategy,which randomly reverses some individuals to improve the global search ability.Thirdly,the chain predation strategy is designed to allow the search agent to be guided by both the best individual and the previous individual.Finally,this paper proposes a rotation predation strategy,which regards the position of the current best individual as the pivot and rotate other members for enhacing the exploitation ability.To verify the performance of the proposed technique,MSGWO is compared with seven state-of-the-art meta-heuristics and four variant GWO algorithms on CEC2022 benchmark functions and three engineering optimization problems.The results demonstrate that MSGWO has better performance on most of benchmark functions and shows competitive in solving engineering design problems.

关 键 词：Grey wolf optimizer variable weights reverse learning chain predation rotation predation 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]