Improving PSO-based multiobjective optimization using competition and immunity clonal

被引：0
|
作者：
Zhang, XH [1 ]
Meng, HY
Jiao, LC
机构：
[1] Xidian Univ, Inst Intelligent Informat Proc, Xian 710071, Peoples R China
[2] Xidian Univ, Dept Appl Math, Xian 710071, Peoples R China
来源：
COMPUTATIONAL INTELLIGENCE AND SECURITY, PT 1, PROCEEDINGS | 2005年 / 3801卷
关键词：
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：
An Intelligent Particle Swarm Optimization (IPSO) for MO problems is proposed based on AER (Agent-Environment-Rules) model, in which Competition and Clonal Selection operator are designed to provide an appropriate selection pressure to propel the swarm population towards the Pareto-optimal front. Simulations and comparison with NSGA-II and MOPSO indicate that IPSO is highly competitive.
引用
收藏
页码：839 / 845
页数：7
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