A Particle Swarm Optimization Algorithm with Logarithm Decreasing Inertia Weight and Chaos Mutation

被引：55
|
作者：
Gao Yue-lin [1 ]
An Xiao-hui [2 ]
Liu Jun-min [2 ]
机构：
[1] North Natl Univ, Res Inst Informat & Syst Sci, Yin Chuan 750021, Peoples R China
[2] Ningzia Univ, Sch Mat Comp, Yin Chuan 750021, Peoples R China
来源：
2008 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY, VOLS 1 AND 2, PROCEEDINGS | 2008年
关键词：
D O I：
10.1109/CIS.2008.183
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A new particle swarm optimal algorithm is proposed in this paper. In the algorithm, logarithm decreasing inertia weight is introduced to improve the convergence speed Further, in order to overcome the disadvantage of easily getting into premature, the diversity of the population is increased by using a chaos mutation. The experiments demonstrate that the new algorithm is better than the particle swarm optimization with linearly decreasing inertia weight not only in convergence speed but also in accuracy.
引用
收藏
页码：61 / +
页数：2
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