灰狼优化算法研究综述  

Comprehensive Review of Grey Wolf Optimization Algorithm

作　　者：张晓凤 王秀英[1] ZHANG Xiao-feng;WANG Xiu-ying(College of Information Science and Technology,Qingdao University of Science and Technology,Qingdao,Shandong 266000,China)

机构地区：[1]青岛科技大学信息科学技术学院,山东青岛266000

出　　处：《计算机科学》2019年第3期30-38,共9页Computer Science

基　　金：国家自然科学基金项目(61773107;61403071)资助

摘　　要：灰狼优化(Grey Wolf Optimization,GWO)算法是一种新兴的群体智能优化算法,因简单高效而被成功应用于诸多领域。文章阐述了灰狼优化算法的搜索机制和实现过程,分析灰狼优化算法的特性,对目前GWO算法的相关改进及应用进行综述。重点对GWO算法的改进策略,包括种群初始化的改进、搜索机制的改进、参数的改进等进行了描述,对GWO算法在参数优化、复杂函数优化和组合优化等方面的应用进行了讨论。最后,对GWO算法的未来改进策略和实际应用进行了展望。Grey wolf optimization (GWO) algorithm is a new kind of swarm-intelligence-based algorithm and some significant developments have been made since its introduction in 2014.GWO has been successfully applied in a variety of fields due to its simplicity and efficiency.This paper provided a complete survey on GWO,including its search mechanism,implementation process,relative merits,improvements and applications.The studies on GWO about its improvements including improvement of population initialization,search mechanism,and parameters were especially discussed.The application status of GWO in aspect of parameter optimization combinatorial optimization and complex function optimization was summarized.Finally,some novel research directions for future development of this powerful algorithm were given.

关 键 词：灰狼优化算法 群体智能 搜索机制 参数优化 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]