A hybrid differential evolution algorithm solving complex multimodal optimization problems

被引：0

作者：

You, Xuemei ^{[1
,2
]}

Hao, Fanchang ^{[3
,4
]}

Ma, Yinghong ^{[1
]}

机构：

[1] School of Management Science and Engineering, Shandong Normal University, Jinan

[2] The Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei

[3] School of Information, Shandong University of Political Science and Law, Jinan

[4] The Evidence Forensic Laboratory in Universities of Shandong Province, Jinan

来源：

Journal of Information and Computational Science | 2015年 / 12卷 / 13期

关键词：

Adaptive Parameter; Differential Evolution; Multimodal; Mutation; Opposition-based Learning; Optimization Problem; Strategy;

D O I：

10.12733/jics20106362

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Differential Evolution (DE) algorithm is a kind of intelligent algorithm based on natural evolution over the past few decades, which shows better optimization performance on some classical benchmark problems. However, it is easy to fall into local optimum and hard to find the global optimal solution when solving complex multimodal optimization problems. In this paper, we propose a Hybrid-strategybased Differential Evolution (HDE) algorithm. It combines three search strategies, the self-adaptive controlling of parameters, multiple mutation strategy and opposition-based learning. Then we select 11 different types of complex multimodal optimization problems and do experiment with simulated data. The result shows that HDE algorithm gets better performance than other 7 methods. ©, 2015, Journal of Information and Computational Science. All right reserved.

引用

页码：5175 / 5182

页数：7

共 12 条

[11]

Van Den Bergh F., Engelbrecht A.P., A cooperative approach to particle swarm optimization, IEEE Trans. Evol. Comput, 8, pp. 225-239, (2004)

[12]

Garcia S., Fernandez A., Luengo J., Herrera F., Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power, Information Sciences, 180, pp. 2044-2064, (2010)

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