Training feedforward neural networks using multi-phase particle swarm optimization

B. Al-Kazemi, Chilukuri K Mohan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

52 Scopus citations

Abstract

The multi-phase particle swarm optimization algorithm (MPPSO) is a variant of the particle swarm optimization algorithm. It simultaneously evolves multiple groups of particles that change their search criterion when changing the phases, and also incorporates hill-climbing. This paper examines the applicability of MPPSO in training feedforward neural network.

Original languageEnglish (US)
Title of host publicationICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2615-2619
Number of pages5
Volume5
ISBN (Electronic)9810475241, 9789810475246
DOIs
StatePublished - 2002
Event9th International Conference on Neural Information Processing, ICONIP 2002 - Singapore, Singapore
Duration: Nov 18 2002Nov 22 2002

Other

Other9th International Conference on Neural Information Processing, ICONIP 2002
CountrySingapore
CitySingapore
Period11/18/0211/22/02

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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  • Cite this

    Al-Kazemi, B., & Mohan, C. K. (2002). Training feedforward neural networks using multi-phase particle swarm optimization. In ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age (Vol. 5, pp. 2615-2619). [1201969] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICONIP.2002.1201969