Title :
Genetic enhancing chaotic particle swarm optimization algorithm
Author_Institution :
Coll. of Electr. Eng., Henan Univ. of Technol., Zhengzhou, China
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;
Conference_Titel :
Control Conference (CCC), 2010 29th Chinese
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-6263-6
