An Improved Hybrid Multi-objective Particle Swarm Optimization Algorithm

被引：0

作者：

Zhou, Zuan ^{[1
]}

Dai, Guangming ^{[1
]}

Fang, Pan ^{[1
]}

Chen, Fangjie ^{[1
]}

Tan, Yi ^{[1
]}

机构：

[1] China Univ Geosci, Sch Comp, Wuhan 430074, Peoples R China

来源：

ADVANCES IN COMPUTATION AND INTELLIGENCE, PROCEEDINGS | 2008年 / 5370卷

关键词：

Particle swarm optimization; multi-objective optimization; orthogonal initialization; Cauchy mutation;

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 is a promising evolutionary optimization algorithm. In this paper, an improved hybrid multi-objective particle swarm optimization algorithm (IHMOPSO) is proposed. IHMOPSO uses orthogonal design to initialize Population, selects global optimal position from Pareto set. Apply mutation, cross operation and evolutionary selection, and uses two ways to update the position and velocity of particles. Experimental results on many well-known benchmark optimization problems have shown that IHMOPSO is effective and efficient.

引用

页码：181 / 188

页数：8

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