A Hybrid Particle Swarm Algorithm for Nonlinear Parameter Estimation

Author

Pei, Shengyu ; Zhou, Yongquan ; Luo, Qifang

Author_Institution

Coll. of Math. & Comput. Sci., Guangxi Univ. for Nat., Nanning, China

Volume

1

fYear

2009

fDate

10-11 Oct. 2009

Firstpage

219

Lastpage

222

Abstract

A hybrid particle swarm optimization algorithm for solving non-linear parameter estimation is proposed, which is based on genetic algorithm. And can increase the diversity of population and make the particles have a definite evolution direction when they are renewed. This improved approach has been tested on some problems commonly used in this paper. The results show that the proposed approach is an efficient and can reach a higher precision.

Keywords

genetic algorithms; nonlinear estimation; parameter estimation; particle swarm optimisation; crossover operator; evolution direction; genetic algorithm; hybrid particle swarm optimization algorithm; nonlinear parameter estimation; Automation; Computer science; Control theory; Educational institutions; Genetic algorithms; Mathematics; Optimization methods; Parameter estimation; Particle swarm optimization; Three-term control; crossover operator; genetic algorithm; parameters estimation; particle swarm optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on

Conference_Location

Changsha, Hunan

Print_ISBN

978-0-7695-3804-4

Type

conf

DOI

10.1109/ICICTA.2009.61

Filename

5287671