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

页码：1 / 5

页数：5

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