Cauchy mutation based on objective variable of Gaussian particle swarm optimization for parameters selection of SVM

Qi Wu, Chun Hung Roberts Law

Research output: Journal article publicationJournal articleAcademic researchpeer-review

30 Citations (Scopus)


On the basis of the slow convergence of particle swarm algorithm (PSO) during parameters selection of support vector machine (SVM), this paper proposes a hybrid mutation strategy that integrates Gaussian mutation operator and Cauchy mutation operator for PSO. The combinatorial mutation based on the fitness function value and the iterative variable is also applied to inertia weight. The results of application in parameter selection of support vector machine show the proposed PSO with hybrid mutation strategy based on Gaussian mutation and Cauchy mutation is feasible and effective, and the comparison between the method proposed in this paper and other ones is also given, which proves this method is better than sole Gaussian mutation and standard PSO.
Original languageEnglish
Pages (from-to)6405-6411
Number of pages7
JournalExpert Systems with Applications
Issue number6
Publication statusPublished - 1 Jun 2011


  • Cauchy mutation
  • Gaussian mutation
  • Particle swarm optimization
  • Support vector machine

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Engineering(all)

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