A Diversity Guided Particle Swarm Optimization with Chaotic Mutation

被引：0
|
作者：
Yang, Yanping [1 ]
Che, Yonghe [2 ]
机构：
[1] Hebei Normal Univ Sci & Technol, Dept Comp Sci, Qinhuangdao 066004, Peoples R China
[2] Hebei Normal Univ Sci & Technol, Qinhuangdao 066004, Peoples R China
来源：
2010 2ND INTERNATIONAL ASIA CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS (CAR 2010), VOL 2 | 2010年
关键词：
particle swarm optimization (PSO); evolutionary computation; global optimization;
D O I：
10.1109/CAR.2010.5456542
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Particle Swarm Optimization (PSO) as well as genetic algorithm has shown good search abilities in many optimization problems. However, PSO easily falls into local minima on complex problems because of the loss of swarm diversity. This paper presents an improved diversity guided PSO algorithm, called DCPSO, by employing a modified velocity model and a chaotic mutation operator. In order to verify the performance of DCPSO, we test it on six benchmark functions. The simulation results show that DCPSO outperforms other two variants of PSO in all test cases.
引用
收藏
页码：294 / 297
页数：4
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