计算机科学 ›› 2019, Vol. 46 ›› Issue (3): 30-38.doi: 10.11896/j.issn.1002-137X.2019.03.004

• 综述 • 上一篇    下一篇

灰狼优化算法研究综述

张晓凤,王秀英   

  1. 青岛科技大学信息科学技术学院 山东 青岛 266000
  • 收稿日期:2018-04-08 修回日期:2018-07-11 出版日期:2019-03-15 发布日期:2019-03-22
  • 通讯作者: 王秀英女,教授,硕士生导师,主要研究方向为生产计划与调度理论和方法、智能优化算法,E-mail:bywxy@126.com
  • 作者简介:张晓凤女,硕士生,CCF会员,主要研究方向为智能优化算法、生产计划与生产调度,E-mail:janezqd@163.com
  • 基金资助:
    本文受国家自然科学基金项目(61773107,61403071)资助

Comprehensive Review of Grey Wolf Optimization Algorithm

ZHANG Xiao-feng, WANG Xiu-ying   

  1. College of Information Science and Technology,Qingdao University of Science and Technology,Qingdao,Shandong 266000,China
  • Received:2018-04-08 Revised:2018-07-11 Online:2019-03-15 Published:2019-03-22

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。

关键词: 参数优化, 灰狼优化算法, 群体智能, 搜索机制

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.

Key words: Grey wolf optimization algorithm, Parameter optimization, Search mechanism, Swarm intelligence

中图分类号: 

  • TP301
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