A hybrid particle swarm optimization and its application in neural networks

S. Y S Leung, Yang Tang, Wai Keung Wong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

59 Citations (Scopus)


In this paper, a novel particle swarm optimization model for radial basis function neural networks (RBFNN) using hybrid algorithms to solve classification problems is proposed. In the model, linearly decreased inertia weight of each particle (ALPSO) can be automatically calculated according to fitness value. The proposed ALPSO algorithm was compared with various well-known PSO algorithms on benchmark test functions with and without rotation. Besides, a modified fisher ratio class separability measure (MFRCSM) was used to select the initial hidden centers of radial basis function neural networks, and then orthogonal least square algorithm (OLSA) combined with the proposed ALPSO was employed to further optimize the structure of the RBFNN including the weights and controlling parameters. The proposed optimization model integrating MFRCSM, OLSA and ALPSO (MOA-RBFNN) is validated by testing various benchmark classification problems. The experimental results show that the proposed optimization method outperforms the conventional methods and approaches proposed in recent literature.
Original languageEnglish
Pages (from-to)395-405
Number of pages11
JournalExpert Systems with Applications
Issue number1
Publication statusPublished - 1 Jan 2012


  • Fisher ratio class separability measure (FRCSM)
  • Markov chain
  • Orthogonal least square algorithm (OLSA)
  • Particle swarm optimization
  • Radial basis function neural networks (RBFNNs)

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • General Engineering


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