Study on Immune PSO Hybrid Optimization Algorithm

被引：0

作者：

Hong, Lu ^{[1
,2
]}

Ji, Zhi-Cheng ^{[1
]}

Gong, Cheng-Long ^{[2
]}

机构：

[1] Jiangnan Univ, Sch Commun & Control Engn, Wuxi 214122, Peoples R China

[2] Huaihai Inst Technol, Sch Elect Engn, Liayungang 222005, Peoples R China

来源：

PROCEEDINGS OF THE 2009 CHINESE CONFERENCE ON PATTERN RECOGNITION AND THE FIRST CJK JOINT WORKSHOP ON PATTERN RECOGNITION, VOLS 1 AND 2 | 2009年

关键词：

particle swarm optimization; immune mechanism; diversity; premature convergence;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Particle swarm optimization (PSO) has poor diversity, slow convergence speed and is easy to trap into local optimum during the course of searching, a modified particle swarm optimization algorithm based on immune mechanism is proposed. The new algorithm has both the properties of the original particle swarm optimization algorithm and the immune diversity keeping mechanism, and can improve the abilities of seeking the global optimum and evolution speed. The simulation results of multi-modal function optimization show that the proposed algorithm can inhibit premature effectively and has preferable global convergent performance.

引用

页码：59 / +

页数：2

共 7 条

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