Particle Swarm Optimization Algorithm Based on Two Swarm Evolution

被引：0

作者：

Wang Li ^{[1
]}

Zhang Jianfeng ^{[2
]}

Li Xin ^{[2
]}

Sun Guoqiang ^{[2
]}

机构：

[1] Univ Air Force, Engn Coll Aeronaut & Astronaut, Xian 710038, Peoples R China

[2] Aviat Univ Air Force, Changchun 130022, Peoples R China

来源：

2015 27TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2015年

关键词：

Particle swarm; Inertia weight; Swarm intelligence; Optimization;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A new particle swarm optimization algorithm that based on two swarm's evolution is proposed. In one swarm the linear decreasing weight is used, in the other swarm the random inertia weight is adopted. The random disturbance is added to the formula of position update. During the running time, a new swarm is generated by the contest of two swarm's evolution. The ability of particle swarm optimization algorithm to break away from the local optimum is improved greatly. The experiment results show that the new algorithm can greatly improve the global convergence ability and enhance the rate of convergence.

引用

页码：1200 / 1204

页数：5