【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。更多还原

【Abstract】 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.更多还原