An Improved Grey Wolf Optimization Algorithm

被引：0

作者：

Long W. ^{[1
,2
]}

Cai S.-H. ^{[1
]}

Jiao J.-J. ^{[2
]}

Wu T.-B. ^{[3
]}

机构：

[1] Key Laboratory of Economics System Simulation, Guizhou University of Finance & Economics, Guiyang, 550025, Guizhou

[2] School of Mathematics and Statistics, Guizhou University of Finance & Economics, Guiyang, 550025, Guizhou

[3] School of Energy and Electrical Engineering, Hunan University of Humanities Science & Technology, Loudi, 417000, Hunan

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2019年 / 47卷 / 01期

关键词：

Chaotic initialization; Control parameter; Differential evolution; Grey wolf optimization algorithm; Particle swarm optimization;

D O I：

10.3969/j.issn.0372-2112.2019.01.022

中图分类号：

学科分类号：

摘要：

Grey wolf optimization (GWO) algorithm is a relatively novel optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in canonical GWO regarding its position update equation, which is good at exploitation but poor at exploration. Inspired by differential evolution and particle swarm optimization, the personal best information and the random selected individual from population are used to construct a modified position update equation for enhancing the exploration. Inspired by particle swarm optimization, a random adjustment strategy of control parameterais proposed. In addition, to enhance the global convergence, when producing the initial population, the chaos method is employed. Simulation experiments were conducted on the 18 high-dimensional conventional test functions. The simulation results show that the proposed algorithm provides better performance than basic GWO algorithms in the same or less number of maximum fitness function evaluation in most cases. © 2019, Chinese Institute of Electronics. All right reserved.

引用

页码：169 / 175

页数：6

共 13 条

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