Improving the performance of particle swarm optimization using adaptive critics designs

被引：0
|
作者：
Doctor, S [1 ]
Venayagamoorthy, GK [1 ]
机构：
[1] Univ Missouri, Dept Elect & Comp Engn, Real Time Power & Intelligent Syst Lab, Rolla, MO 65409 USA
来源：
2005 IEEE SWARM INTELLIGENCE SYMPOSIUM | 2005年
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：
Swarm intelligence algorithms are based on natural behaviors. Particle swarm optimization (PSO) is a stochastic search and optimization tool. Changes in the PSO parameters, namely the inertia weight and the cognitive and social acceleration constants, affect the performance of the search process. This paper presents a novel method to dynamically change the values of these parameters during the search. Adaptive critic design (ACD) has been applied for dynamically changing the values of the PSO parameters.
引用
收藏
页码：393 / 396
页数：4
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