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

Author

Yue-Lin Gao ; Xiao-hui An ; Jun-min Liu

Author_Institution

Res. Inst. of Inf. & Syst. Sci., North Nat. Univ., Yin Chuan, China

Volume

1

fYear

2008

fDate

13-17 Dec. 2008

Firstpage

61

Lastpage

65

Abstract

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.

Keywords

chaos; particle swarm optimisation; chaos mutation; convergence speed; logarithm decreasing inertia weight; particle swarm optimization algorithm; Birds; Chaos; Computational intelligence; Computer security; Convergence; Genetic mutations; Mathematics; National security; Particle swarm optimization; Testing; Chaos mutation; global optimization; logarithm decreasing inertia weight; particle swarm optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Security, 2008. CIS '08. International Conference on

Conference_Location

Suzhou

Print_ISBN

978-0-7695-3508-1

Type

conf

DOI

10.1109/CIS.2008.183

Filename

4724615