Hybrid particle swarm optimization with wavelet mutation and its industrial applications

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

218 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)743-763
Number of pages21
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume38
Issue number3
DOIs
Publication statusPublished - 1 Jun 2008

Keywords

  • Load flow problem
  • Modeling
  • Mutation operation
  • Neural network control
  • Particle swarm optimization
  • Wavelet theory

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Medicine(all)
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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