Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1435
Title: A new hybrid particle swarm optimization with wavelet theory based mutation operation
Authors: Ling, SH
Yeung, CW
Chan, KY
Iu, HHC
Leung, FHF 
Keywords: Associative storage
Convergence of numerical methods
Function evaluation
Neural networks
Quality control
Wavelet transforms
Issue Date: 2007
Publisher: IEEE
Source: CEC 2007 : IEEE Congress on Evolutionary Computation, Singapore, 25–28 September 2007, p. 1977-1984 How to cite?
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.
URI: http://hdl.handle.net/10397/1435
ISBN: 1-4244-1340-0
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