Improved whale optimization algorithm for large scale optimization problems

被引：0

作者：

Long W. ^{[1
,2
]}

Cai S. ^{[1
]}

Jiao J. ^{[2
]}

Tang M. ^{[3
]}

Wu T. ^{[4
]}

机构：

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

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

[3] School of Energy and Power Engineering, Changsha University of Science and Engineering, Changsha

[4] School of Energy and Electrical Engineering, Hunan University of Humanities, Science and Technology, Loudi

来源：

| 1600年 / Systems Engineering Society of China卷 / 37期

基金：

中国国家自然科学基金;

关键词：

Diversity mutation; Nonlinear convergence factor large scale optimization problem; Opposition-based learning strategy; Whale optimization algorithm;

D O I：

10.12011/1000-6788(2017)11-2983-12

中图分类号：

学科分类号：

摘要：

An improved whale optimization algorithm (WOA) based on nonlinear convergence factor, named IWOA, is proposed for solving large scale complicated optimization problems. In the proposed algorithm, opposition-based learning strategy is used to initial the whale individuals' position in the search space, which strengthened the diversity of individuals in the global searching process. A novel nonlinearly update equation of convergence factor is designed to coordinate the abilities of exploration and exploitation. It then disturbed the current optimal individual by diversity mutation operator in the process of the search so as to avoid the possibility of falling into local optimum. Simulation experiments were conducted on the 15 large scale (200, 500, and 1000 dimension) conventional test functions. The experimental results show that the proposed IWOA has better performance in solution precision and convergence rate than other comparison methods. © 2017, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.

引用

页码：2983 / 2994

页数：11

共 23 条

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