A hybrid particle swarm optimization method

被引：7
|
作者：
Wang, X. [1 ]
Gao, X. Z. [1 ]
Ovaska, S. J. [1 ]
机构：
[1] Aalto Univ, Inst Intelligent Power Elect, Otakaari 5A, FI-02150 Espoo, Finland
来源：
2006 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-6, PROCEEDINGS | 2006年
基金：
芬兰科学院;
关键词：
D O I：
10.1109/ICSMC.2006.384785
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 Particle Swarm Optimization (PSO) method, which is based on the fusion of the PSO, Clonal Selection Algorithm (CSA), and Mind Evolutionary Computation (MEC). The clone function borrowed from the CSA and MEC-characterized similartaxis and dissimilation operations are embedded in the original PSO. Simulations of nonlinear function optimization are made to compare this hybrid PSO with the regular PSO. It has been demonstrated that our hybrid algorithm can achieve a better convergence performance, and provide diverse solutions to multi-model optimization problems.
引用
收藏
页码：4151 / +
页数：3
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