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A New Approach to Improve Particle Swarm Optimization
Liping Zhang, Huanjun Yu, and Shangxu Hu
College of Material and Chemical Engineering,
Zhejiang University,
Hangzhou 310027, P.R. China
zhanglp@infotech.zju.edu.cn
yuhj@zjuem.zju.edu.cn
sxhu@mail.hz.zj.cn
Abstract. Particle swarm optimization (PSO) is a new evolutionary computation technique. Although PSO algorithm possesses many attractive properties, the methods of selecting inertia weight need to be further investigated. Under this consideration, the inertia weight employing random number uniformly distributed in [0,1] was introduced to improve the performance of PSO algorithm in this work. Three benchmark functions were used to test the new method. The results were presented to show that the new method is effective.
LNCS 2723, p. 134 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003