An improved cooperative particle swarm optimization and its application

被引:17
|
作者
Chen, Debao [1 ]
Zhao, Chunxia [2 ]
Zhang, Haofeng [2 ]
机构
[1] Huai Bei Normal Univ, Sch Phys & Elect Informat, Huaibei 235000, Peoples R China
[2] NanJing Univ Sci & Technol, Comp Inst, Nanjing 210094, Peoples R China
来源
NEURAL COMPUTING & APPLICATIONS | 2011年 / 20卷 / 02期
关键词
Particle swarm optimization (PSO); Multi-cooperative particle swarm (MCPSO); Evolutionary computation (EC); Artificial neural network (ANN);
D O I
10.1007/s00521-010-0503-4
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A powerful cooperative evolutionary particle swarm optimization (PSO) algorithm based on two swarms with different behaviors to improve the global performance of PSO is proposed. In this method, one swarm tracks the best position and the other leaves the worst position of them; the best and the worst solutions of the two swarms are exchanged in the common blackboard and the information can be flowed mutually between them. The diversity is maintained if the two swarms are regarded as a whole. To show the effectiveness of the given algorithm, five benchmark functions and two forward ANNs with three layers are performed; the results of the proposed algorithms are compared with standard PSO, MCPSO and NPSO.
引用
收藏
页码:171 / 182
页数:12
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