文章基本信息

标题：Improved Grey Wolf Optimizer with Differential Perturbation for Function Optimization
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作者：Qiang Qu ; Hai-hua Wang ; Mei-li Qi 等
期刊名称：Lecture Notes in Engineering and Computer Science
印刷版ISSN：2078-0958
电子版ISSN：2078-0966
出版年度：2022
卷号：52
期号：2
语种：English
出版社：Newswood and International Association of Engineers
摘要：Grey wolf optimizer (GWO) is a biological?inspired optimization algorithm with the advantages of few parameters, robustness, and easy implementation. It is widely used to solve the function optimization problem. However, GWO can easily fall into local optimum and can suffer from premature convergence. In this study, an improved GWO with differential perturbation, called IGWO, is presented. First, a non-linear reduction strategy is used instead of the linear reduction strategy in GWO to update the convergence factor, which increases the global search capability of IGWO. In addition, a random differential perturbation strategy with strong exploitation capability is embedded in GWO to increase the diversity of the population and ensure the local exploitation capability of IGWO. Finally, IGWO is tested with 16 benchmark functions. The simulation results show that IGWO outperforms PSO, GSA, GWO, ALO, MFO, mGWO, DE-GWO, wd-GWO, and HGWO in terms of convergence accuracy and convergence speed.
关键词：grey wolf optimizer; function optimization;differential perturbation; non-linear reduction strategy