An improved particle swarm optimization with new select mechanism

被引:1
|
作者
Jiang, Yi [1 ]
Yue, Qingling [2 ]
机构
[1] Wuhan Univ Sci & Technol, Sch Comp Sci, Wuhan 430081, Peoples R China
[2] Hubei Univ, Dept Archive, Wuhan 430062, Peoples R China
来源
FIRST INTERNATIONAL WORKSHOP ON KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS | 2007年
关键词
D O I
10.1109/WKDD.2008.71
中图分类号
F [经济];
学科分类号
02 ;
摘要
The particle swarm optimization is a stochastic, population-based optimization technique. A modified PSO algorithm is proposed in this paper to avoid premature convergence with the new select mechanism. This mechanism is simulating the principle of molecular dynamics, which attempts to active all particles as the most possible along with their population evolving. Two stopping criteria of the algorithm are derived from the principle of energy minimization and the law of entropy increasing. The performance of this algorithm is compared to the standard PSO algorithm and experiments indicate that it has better performance.
引用
收藏
页码:383 / +
页数:3
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