A new hybrid particle swarm optimization with wavelet theory based mutation operation

S. H. Ling; C. W. Yeung; K. Y. Chan; H. H.C. Iu; Hung Fat Frank Leung

doi:10.1109/CEC.2007.4424716

A new hybrid particle swarm optimization with wavelet theory based mutation operation

S. H. Ling
, C. W. Yeung
, K. Y. Chan
, H. H.C. Iu
, Hung Fat Frank Leung

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

38 Citations (Scopus)

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.

Original language	English
Title of host publication	2007 IEEE Congress on Evolutionary Computation, CEC 2007
Pages	1977-1984
Number of pages	8
DOIs	https://doi.org/10.1109/CEC.2007.4424716
Publication status	Published - 1 Dec 2007
Event	2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore Duration: 25 Sept 2007 → 28 Sept 2007

Conference

Conference	2007 IEEE Congress on Evolutionary Computation, CEC 2007
Country/Territory	Singapore
Period	25/09/07 → 28/09/07

ASJC Scopus subject areas

Artificial Intelligence
Software
Theoretical Computer Science

More information

10.1109/CEC.2007.4424716

http://hdl.handle.net/10397/1435

Cite this

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AB - 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.

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M3 - Conference article published in proceeding or book

SN - 1424413400

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Y2 - 25 September 2007 through 28 September 2007

ER -

A new hybrid particle swarm optimization with wavelet theory based mutation operation

Abstract

Conference

ASJC Scopus subject areas

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