Genetic enhancing chaotic particle swarm optimization algorithm

Author

Zhao Liang

Author_Institution

Coll. of Electr. Eng., Henan Univ. of Technol., Zhengzhou, China

fYear

2010

fDate

29-31 July 2010

Firstpage

5182

Lastpage

5187

Abstract

This paper presents a genetic enhancing chaotic particle swarm optimization algorithm(GECPSO)by integrating the particle swarm optimization(PSO),genetic algorithm (GA)and chaos search algorithm. First, the GA is used to enhance the local optimum L Best in PSO. Then, the PSO algorithm parameters c₁,c₂ and w are adjusted using logistic chaotic time series. Furthermore, the effectiveness of GECPSO is evaluated by using five benchmark test functions. Finally the GECPSO is used to adjust the TSK fuzzy neural network parameters for system identification.

Keywords

chaos; fuzzy neural nets; genetic algorithms; identification; particle swarm optimisation; search problems; time series; LBest; TSK fuzzy neural network; chaos search algorithm; genetic algorithm; genetic enhancing chaotic particle swarm optimization; logistic chaotic time series; system identification; Benchmark testing; Chaos; Electronic mail; Genetics; Noise measurement; Particle swarm optimization; Time series analysis; Algorithm evaluation; Benchmark test functions; Genetic enhancing chaotic particle swarm optimization algorithm(GECPSO); System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (CCC), 2010 29th Chinese

Conference_Location

Beijing

Print_ISBN

978-1-4244-6263-6

Type

conf

Filename

5572400