Particle swarm optimization with adaptive parameters

被引：0
|
作者：
Yang, Dongyong [1 ]
Chen, Jinyin [1 ]
Matsumoto, Naofumi [2 ]
机构：
[1] Zhejiang Univ Technol, Dept Software, Zhengzhou, Peoples R China
[2] Ashikaga Inst Technol, Dept Ind & Inform Syst Engn, Ashikaga, Tochigi, Japan
来源：
SNPD 2007: EIGHTH ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING, AND PARALLEL/DISTRIBUTED COMPUTING, VOL 1, PROCEEDINGS | 2007年
关键词：
D O I：
10.1109/SNPD.2007.47
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Particle swarm optimization is an effective evolution algorithm for global optimizing. Based on analysis of particle movements during evolution, parameter p is brought up to control the value of C-1 and C-2, which effects convergence rate of PSO. Aiming at solving different problems, corresponding p is adopted to improve performance. Particle confidence coefficient q is applied to weigh proper emphasize on itself best solution and global solution. Adaptive value of q is introduced to PSO to satisfy specific situation for each particle. Finally, performance of PSO with parameters p and q is testified by optimizing benchmark functions.
引用
收藏
页码：616 / +
页数：2
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