Improved grey wolf optimization algorithm with logarithm function describing convergence factor and its application

被引：5
|
作者：
Wu T. [1 ,2 ]
Gui W. [1 ]
Yang C. [1 ]
Long W. [3 ]
Li Y. [1 ]
Zhu H. [1 ]
机构：
[1] School of Information Science and Engineering, Central South University, Changsha
[2] College of Energy and Electrical Engineering, Hunan University of Humanities, Science and Technology, Loudi
[3] Key Laboratory of Economics System Simulation, Guizhou University of Finance and Economics, Guiyang
来源：
Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) | 2018年 / 49卷 / 04期
基金：
湖南省自然科学基金; 中国国家自然科学基金;
关键词：
Convergence factor; Grey wolf optimization algorithm; Logarithm function;
D O I：
10.11817/j.issn.1672-7207.2018.04.012
中图分类号：
学科分类号：
摘要：
The grey wolf optimization (GWO) algorithm has a few disadvantages such as low precision and high possibility of being trapped in local optimum, an improved GWO algorithm was proposed for solving high-dimensional optimization problem based on the convergence factor about logarithmic function. An initial population was generated based on good point set method to assure that the individuals were distributed in the search space as uniformly as possible. A nonlinear convergence factor was proposed based on logarithm function to balance the exploration ability and exploitation ability. Improved elite opposition-based learning strategy was used to avoid premature convergence of GWO algorithm. Benchmark functions and parameters optimization of real application were employed to verify the performance of the improved GWO algorithm. The results show that the proposed algorithm has better performance. © 2018, Central South University Press. All right reserved.
引用
收藏
页码：857 / 864
页数：7
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