Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSMCB.2008.921005
Title: Hybrid particle swarm optimization with wavelet mutation and its industrial applications
Authors: Ling, S.H. 
Iu, H.H.C.
Chan, K.Y.
Lam, H.K.
Yeung, B.C.W.
Leung, F.H.
Keywords: Load flow problem
Modeling
Mutation operation
Neural network control
Particle swarm optimization
Wavelet theory
Issue Date: Jun-2008
Source: Ling, S.H., Iu, H.H.C., Chan, K.Y., Lam, H.K., Yeung, B.C.W., Leung, F.H. (2008-06). Hybrid particle swarm optimization with wavelet mutation and its industrial applications. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 38 (3) : 743-763. ScholarBank@NUS Repository. https://doi.org/10.1109/TSMCB.2008.921005
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. © 2008 IEEE.
Source Title: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
URI: http://scholarbank.nus.edu.sg/handle/10635/56235
ISSN: 10834419
DOI: 10.1109/TSMCB.2008.921005
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