Particle Swarm Optimization: Dynamic Parameter Adjustment Using Swarm Activity

被引：0
|
作者：
Iwasaki, Nobuhiro [1 ]
Yasuda, Keiichiro [1 ]
Ueno, Genki [1 ]
机构：
[1] Tokyo Metropolitan Univ, Grad Sch Sci & Engn, Dept Elect & Elect Engn, Hachioji, Tokyo 1920397, Japan
来源：
2008 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC), VOLS 1-6 | 2008年
关键词：
Swarm Intelligence; Metaheuristics; Global Optimization; Particle Swarm Optimization; Parameter Adjustment;
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：
In this paper, swarm activity, which is a new index for assessing the diversification (global search) and intensification (local search) during Particle Swarm Optimization (PSO) searches, is introduced. It is shown that swarm activity allows the quantitative assessment of the diversification and intensification during the PSO search. Using this concept, a new PSO called Activity Feedback PSO (AFPSO) is constructed, which involves feedback based on swarm activity to control diversification and intensification during the search. For each of the 5 benchmark problems, this method is used to determine the globally optimal solutions. These numerical experiments show that AFPSO has generality and effectiveness.
引用
收藏
页码：2633 / 2638
页数：6
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