Improvement of Original Particle Swarm Optimization Algorithm Based on Simulated Annealing Algorithm

被引：1

作者：

Cong Liang ^{[1
]}

Hu Chengquan ^{[1
]}

Guo Zongpeng ^{[1
]}

Jiang Yu ^{[1
]}

Sha Lihua ^{[1
]}

机构：

[1] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China

来源：

PROCEEDINGS OF THE 27TH CHINESE CONTROL CONFERENCE, VOL 6 | 2008年

关键词：

Particle swarm optimization; Simulated annealing; Local optimum;

D O I：

10.1109/CHICC.2008.4605337

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) algorithm is an optimization algorithm in the filed of Evolutionary Computation, which has been applied widely in function optimization, artificial neural networks' training, pattern recognition, fuzzy control and some other fields. Original PSO algorithm could be trapped in the local optimum easily, so in this paper we improved the original PSO algorithm using the idea of simulated annealing algorithm, which makes the PSO algorithm jump out of local optimum. In this paper, two improved strategies was proposed, and after testing and comparing the two improved algorithms with the original PSO algorithm again and again, we conclude at last that efficiency of global searching of the two improved strategies is better than the original PSO.

引用

页码：671 / 676

页数：6

共 6 条

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