A modified particle swarm optimization algorithm and application

被引：0
|
作者：
Zheng, Sheng-Fu [1 ]
Hu, Shan-Li [1 ]
Su, She-Xiong [1 ]
Lin, Chao-Feng [1 ]
Lai, Xian-Wei [1 ]
机构：
[1] Fuzhou Univ, Dept Comp Sci & Technol, Fuzhou 350002, Peoples R China
来源：
PROCEEDINGS OF 2007 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2007年
关键词：
particle swarm optimization; swarm intelligence; comparison shopping; agent;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In order to deal with the problem of slow search speed and premature convergence, a flexible particle swarm optimization algorithm is proposed. Simulations have been done to illustrate that this algorithm can not only significantly speed up the convergence, but also effectively solve the premature convergence problem. Furthermore, the algorithm is applied to neural network's training in the agent model in comparison shopping and the simulation experiment not only shows that compared with related algorithms, the hybrid algorithm which is based on the flexible particle swarm optimization and BP algorithm can quickly converge to a reasonably good solution, but also makes the agent model in comparison shopping more effectively.
引用
收藏
页码：945 / 951
页数：7
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