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

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

36 Citations (Scopus)


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 languageEnglish
Title of host publication2007 IEEE Congress on Evolutionary Computation, CEC 2007
Number of pages8
Publication statusPublished - 1 Dec 2007
Event2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore
Duration: 25 Sep 200728 Sep 2007


Conference2007 IEEE Congress on Evolutionary Computation, CEC 2007

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Theoretical Computer Science

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