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 |
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. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. |
| Appears in Collections: | Conference Paper |
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| Wavelet theory based mutation_07.pdf | 332.12 kB | Adobe PDF | View/Open |
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