Constrained Multi-objective Particle Swarm Optimization Algorithm

被引：0

作者：

Gao, Yue-lin ^{[1
]}

Qu, Min ^{[1
]}

机构：

[1] N Ethn Univ, Inst Informat & Syst Sci, Yinchuan, Ningxia, Peoples R China

来源：

EMERGING INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS | 2012年 / 304卷

关键词：

particle swarm optimization algorithm; constrained multi-objective optimization; Pareto neighborhood crossover operation; penalty function method;

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：

A particle swarm optimization for solving constrained multi-objective optimization problem was proposed (CMPSO). In this paper, the main idea is the use of penalty function to handle the constraints. CMPSO employs particle swarm optimization algorithm and Pareto neighborhood crossover operation to generate new population. Numerical experiments are compared with NSGA-II and MOPSO on three benchmark problems. The numerical results show the effectiveness of the proposed CMPSO algorithm.

引用

页码：47 / 55

页数：9

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