Title
A New Hybrid Particle Swarm Optimization With Wavelet Theory Based Mutation Operation
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.
Year
DOI
Venue
2007
10.1109/CEC.2007.4424716
2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS
Keywords
Field
DocType
neural network,neural nets,associative memory,wavelet transforms
Particle swarm optimization,Convergence (routing),Mathematical optimization,Content-addressable memory,Computer science,Multi-swarm optimization,Artificial intelligence,Content-addressable storage,Artificial neural network,Machine learning,Wavelet,Wavelet transform
Conference
Citations 
PageRank 
References 
13
0.78
1
Authors
5
Name
Order
Citations
PageRank
S. H. Ling160940.29
C. W. Yeung21076.74
K. Y. Chan3130.78
Herbert H. C. Iu433460.21
F. H. Frank Leung518316.00