A mutation particle swarm optimization algorithm for multilayer perceptron training with applications

Z. Chen, J. Wu, Zheru Chi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Particle swarm optimization (PSO), a prevalent optimization algorithm, has been successfully applied to various fields of science and engineering. However, PSO still suffers from some problems such as premature convergence. To solve these problems, we propose a mutation PSO (MPSO) in this paper. Compared with the traditional PSO, there are two main improvements of the proposed MPSO. First, a new particle update rule is explored. The new rule updates a particle's position according to not only its best known position and the global best known position of the swarm, but also a number of other particles' best known positions. The second improvement is that a mutation operator is employed. Mutation operator is used to avoid premature convergence. The MPSO is utilized to train a multilayer perceptron (MLP). The MLP trained by MPSO is finally applied to two classification problems: Iris flower classification and scene classification. For comparison purposes, traditional PSO, genetic algorithm (GA), and back-propagation (BP) are also investigated. Experimental results demonstrate the superior performance of the proposed MPSO for MLP training.
Original languageEnglish
JournalInternational journal of information acquisition
Volume9
Issue number1
DOIs
Publication statusPublished - 2013

Keywords

  • Particle swarm optimization (PSO)
  • Genetic algorithm (GA)
  • Multilayer perceptron (MLP)
  • Iris flower classification
  • Scene classification

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