Hybrid particle swarm optimization with wavelet mutation and its industrial applications

被引：201

作者：

Ling, S. H. ^{[1
]}

Iu, H. H. C. ^{[2
]}

Chan, K. Y. ^{[3
]}

Lam, H. K. ^{[4
]}

Yeung, Benny C. W. ^{[5
]}

Leung, Frank H. ^{[5
]}

机构：

[1] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore

[2] Univ Western Australia, Sch Elect Elect & Comp Engn, Perth, WA 6009, Australia

[3] Hong Kong Polytech Univ, Dept Ind & Syst Engn, Kowloon, Hong Kong, Peoples R China

[4] Kings Coll London, Div Engn, Dept Elect Engn, London WC2R 2LS, England

[5] Hong Kong Polytech Univ, Elect & Informat Engn Dept, Ctr Multimedia Signal Proc, Kowloon, Hong Kong, Peoples R China

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS | 2008年 / 38卷 / 03期

关键词：

load flow problem; modeling; mutation operation; neural network control; particle swarm optimization; wavelet theory;

D O I：

10.1109/TSMCB.2008.921005

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 or 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.

引用

页码：743 / 763

页数：21

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