Particle Swarm Optimization Algorithm

被引:4
|
作者
Zhou, Feihong [1 ]
Liao, Zizhen [2 ]
机构
[1] Hunan Int Econ Univ, Sch Informat Sci & Engn, Changsha, Hunan, Peoples R China
[2] Changsha Human Resources & Social Secur Bur, Informat Ctr, Changsha, Hunan, Peoples R China
来源
SENSORS, MEASUREMENT AND INTELLIGENT MATERIALS, PTS 1-4 | 2013年 / 303-306卷
关键词
PSO; Smooth weight; improved particle swarm; optimization algorithm;
D O I
10.4028/www.scientific.net/AMM.303-306.1369
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The basic and improved algorithms of PSO focus on how to effectively search the optimal solution in the solution space using one of the particle swarm. However, the particles are always chasing the global optimal point and such points currently found on their way of search, rapidly leading their speed down to zero and hence being restrained in the local minimum. Consequently, the convergence or early maturity of particles exists. The improved PSO is based on the enlightenment of BP neural network while the improvement is similar to smooth the weight through low-pass filter. The test of classical functions show that the PSO provides a promotion in the convergence precision and calculation velocity to a certain extent.
引用
收藏
页码:1369 / +
页数:2
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