Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1435
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Title: A new hybrid particle swarm optimization with wavelet theory based mutation operation
Authors: Ling, SH
Yeung, CW
Chan, KY
Iu, HHC
Leung, FHF 
Issue Date: 2007
Source: CEC 2007 : IEEE Congress on Evolutionary Computation, Singapore, 25–28 September 2007, p. 1977-1984
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: Associative storage
Convergence of numerical methods
Function evaluation
Neural networks
Quality control
Wavelet transforms
Publisher: IEEE
ISBN: 1-4244-1340-0
Rights: © 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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