The particle swarm: Parameter selection and convergence

被引：0
|
作者：
Xiao, RenYue [1 ]
Li, Bo [1 ]
He, XuPeng [1 ]
机构：
[1] S China Univ Technol, Sch Math Sci, Guangzhou, Peoples R China
来源：
ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS: WITH ASPECTS OF CONTEMPORARY INTELLIGENT COMPUTING TECHNIQUES | 2007年 / 2卷
关键词：
particle swarm optimization; convergence;
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 particle swarm optimization algorithm is an algorithm to find optimal regions of complex spaces through the interaction of individuals. Convergence analysis and parameter selection in the particle swarm optimization algorithm have been discussed in [2] and [7]. In this paper, the particle swarm optimization algorithm is analyzed further by using standard results from the dynamic system theory. Thus, we derived graphical parameter guidelines from it. Finally, we analyze the convergence of the algorithm by some examples.
引用
收藏
页码：396 / 402
页数：7
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