Immune particle swarm optimization with diversity monitoring

被引：0
|
作者：
Hu, Chunxia [1 ]
Zeng, Jianchao [1 ]
Jie, Jing [1 ]
机构：
[1] Taiyuan Univ Sci & Technol, Div Syst Simulat & Comp Applicat, Taiyuan 030024, Peoples R China
来源：
ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS: WITH ASPECTS OF CONTEMPORARY INTELLIGENT COMPUTING TECHNIQUES | 2007年 / 2卷
基金：
中国国家自然科学基金;
关键词：
Particle Swarm Optimization Algorithm; mechanism of immune system; clone selection; clone suppression; immune memory; recruiting new member;
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：
The paper presents an immune particle swarm optimization with diversity monitoring (DIPSO). In order to maintain appropriate diversity, DIPSO take advantage of immune operators to update the particles at the right moment through monitoring the swarm diversity of each generation. The modified algorithm can avoid the local optimum and has better search performance for multi-peak functions. Testing over the benchmark problems, the experimental results show the modified algorithm has better convergence performance than standard particle swarm optimization algorithm.
引用
收藏
页码：380 / +
页数：3
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