Multi-objective Particle Swarm Optimization Algorithm Based on the Disturbance Operation

被引：0

作者：

Gao, Yuelin ^{[1
]}

Qu, Min ^{[1
]}

机构：

[1] Beifang Univ Nationalities, Inst Informat & Syst Sci, Ningxia 750021, Yinchuan, Peoples R China

来源：

ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, PT I | 2011年 / 7002卷

关键词：

multi-objective optimization; particle swarm optimization; disturbance operation;

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：

To overcome the defect of wide-ranged exploration for particle swarm optimization, a kind of multi-objective particle swarm optimization algorithm with disturbance operation(MPSOD) is proposed. It employs particle swarm optimization and disturbance operation to generate new population in order to enhance the wide-ranged exploration for particle swarm optimization algorithm. Numerical experiments are compared with NSGA-II, SPEA2 and IVIOPSO On six benchmark problems. The numerical results show the effectiveness of the proposed MPSOD algorithm.

引用

页码：591 / 600

页数：10

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