An immune-based ant colony algorithm for static and dynamic optimization

被引：0

作者：

Wang, X. ^{[1
]}

Gao, X. Z. ^{[1
]}

Ovaska, S. J. ^{[2
]}

机构：

[1] Aalto Univ, Inst Intelligent Power Elect, Otakaari 5A, FI-02150 Espoo, Finland

[2] Utah State Univ, Dept Elect & Comp Engn, Logan, UT 84322 USA

来源：

2007 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOLS 1-8 | 2007年

基金：

芬兰科学院;

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper proposes a hybrid optimization method based on the ant colony and clonal selection algorithms, in which the cloning and mutation operations are embedded in the ant colony to enhance its search capability. The novel algorithm is employed to deal with a few benchmark optimization problems under both static and dynamic environments. Simulation results demonstrate the remarkable advantages of our approach in diverse optimal solutions, closely tracking varying optimum, as well as improved convergence speed.

引用

页码：1007 / +

页数：2

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