Neural networks learning using vbest model particle swarm optimisation

Hong B.O. Liu, Y. I.Yuan Tang, Jun Meng, Ye Ji

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

Abstract

The two most commonly used methods are known as gbest model and Ibest model in particle swarm optimization (PSO). The gbest model converges quickly on problem solutions but has a weakness of becoming trapped in local optima, while the Ibest model is able to "flow around" local optima, as the individuals explore different regions. In this paper, we investigated a variable neighborhood model in particle swarm search method for neural network learning, and the experimental results illustrated its efficiency.

Original languageEnglish
Title of host publicationProceedings of 2004 International Conference on Machine Learning and Cybernetics
Pages3157-3159
Number of pages3
StatePublished - 2004
EventProceedings of 2004 International Conference on Machine Learning and Cybernetics - Shanghai, China
Duration: Aug 26 2004Aug 29 2004

Publication series

NameProceedings of 2004 International Conference on Machine Learning and Cybernetics
Volume5

Conference

ConferenceProceedings of 2004 International Conference on Machine Learning and Cybernetics
CountryChina
CityShanghai
Period08/26/0408/29/04

Keywords

  • Learning Algorithm
  • Neural Network
  • Particle Swarm Optimization (PSO)

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