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|Title: ||Hybrid particle swarm optimization with wavelet mutation and its industrial applications|
|Authors: ||Ling, S. H.|
Iu, Herbert Ho-ching
Chan, K. Y.
Lam, H. K.
Yeung, Benny C. W.
Leung, Frank H. F.
|Subjects: ||Load flow problem|
Neural network control
Particle swarm optimization
|Issue Date: ||Jun-2008 |
|Citation: ||IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics, June 2008, v. 38, no. 3, p. 743-763.|
|Abstract: ||A new hybrid particle swarm optimization (PSO) that incorporates a wavelet-theory-based mutation operation is proposed. It applies the wavelet theory to enhance the PSO in exploring the solution space more effectively for a better solution. A suite of benchmark test functions and three industrial applications (solving the load flow problems, modeling the development of fluid dispensing for electronic packaging, and designing a neural-network-based controller) are employed to evaluate the performance and the applicability of the proposed method. Experimental results empirically show that the proposed method significantly outperforms the existing methods in terms of convergence speed, solution quality, and solution stability.|
|Rights: ||© 2008 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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|Type: ||Journal/Magazine Article|
|Appears in Collections:||EIE Journal/Magazine Articles|
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