Title
Hybrid Particle Swarm Optimization With Wavelet Mutation and Its Industrial Applications
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.
Year
DOI
Venue
2008
10.1109/TSMCB.2008.921005
IEEE Transactions on Systems, Man, and Cybernetics, Part B
Keywords
Field
DocType
benchmark testing,wavelet transforms,industry,particle swarm optimization,load flow,electronic packaging,modeling,computer simulation,biomimetics,algorithms,electronics packaging,electronics industry,neural network,space exploration
Convergence (routing),Particle swarm optimization,Mathematical optimization,Control theory,Computer science,Electronic packaging,Multi-swarm optimization,Benchmark (computing),Wavelet,Wavelet transform
Journal
Volume
Issue
ISSN
38
3
1083-4419
Citations 
PageRank 
References 
91
4.61
16
Authors
6
Name
Order
Citations
PageRank
S. H. Ling160940.29
Herbert H. C. Iu233460.21
Kit Yan Chan347045.36
H. K. Lam43618193.15
C. W. Yeung51076.74
F. H. Frank Leung618316.00