Multi-Group Particle Swarm Optimization with Random Redistribution

被引：0
|
作者：
Suryanto, Naufal [1 ]
Ikuta, Chihiro [2 ]
Pramadihanto, Dadet [1 ]
机构：
[1] Elect Engn Polytech Inst Surabaya, Dept Informat & Comp Engn, Surabaya, Indonesia
[2] Anan Natl Coll Technol, Dept Creat Technol Engn, Anan, Japan
来源：
2017 INTERNATIONAL ELECTRONICS SYMPOSIUM ON KNOWLEDGE CREATION AND INTELLIGENT COMPUTING (IES-KCIC) | 2017年
关键词：
Particle Swarm Optimization; Global Optimization; Evolutionary computation;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Particle Swarm Optimization (PSO) is fast and popular algorithm to find the optimum value of non-linear and multi-dimensional function. However, it often easily trapped into local optima because the particles move closer to the best particle quickly. This paper purposes a new algorithm called Multi-Group Particle Swarm Optimization with Random Redistribution (MGRR-PSO) that tried to solve the weakness of standard PSO. MGRR-PSO combines two groups of PSO with opposite acceleration coefficients. In addition, some particles are redistributed when they are trapped in local optima. Experimental studies on 5 benchmark functions with 50-dimensions and 100-dimensions show that the MGRR-PSO can solve the problems that can't be solved by original PSO with better performance.
引用
收藏
页码：1 / 5
页数：5
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