DocumentCode :
2691516
Title :
A new hybrid Particle Swarm Optimization with wavelet theory based mutation operation
Author :
Ling, S.H. ; Yeung, C.W. ; Chan, K.Y. ; Iu, Herbert H. C. ; Leung, F.H.F.
Author_Institution :
Univ. of Western Australia, Crawley
fYear :
2007
fDate :
25-28 Sept. 2007
Firstpage :
1977
Lastpage :
1984
Abstract :
An improved hybrid particle swarm optimization (PSO) that incorporates a wavelet-based mutation operation is proposed. It applies wavelet theory to enhance PSO in exploring solution spaces more effectively for better solutions. A suite of benchmark test functions and an application example on tuning an associative-memory neural network are employed to evaluate the performance of the proposed method. It is shown empirically that the proposed method outperforms significantly the existing methods in terms of convergence speed, solution quality and solution stability.
Keywords :
content-addressable storage; neural nets; particle swarm optimisation; wavelet transforms; associative-memory neural network; hybrid particle swarm optimization; mutation operation; wavelet theory; Costs; Evolutionary computation; Genetic mutations; Particle swarm optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2007. CEC 2007. IEEE Congress on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-1339-3
Electronic_ISBN :
978-1-4244-1340-9
Type :
conf
DOI :
10.1109/CEC.2007.4424716
Filename :
4424716
Link To Document :
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