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Hybrid particle swarm optimization and GMDH system

Sharma, Anuraganand and Onwubolu, Godfrey C. (2009) Hybrid particle swarm optimization and GMDH system. In: Hybrid Self-Organizing Modeling Systems. Studies in Computational Intelligence, 211 . Springer Berlin Heidelberg, Berlin Heidelberg, pp. 193-231. ISBN Print 978-3-642-01529-8 Online 978-3-642-01530-4

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    Abstract

    This chapter describes a new design methodology which is based on hybrid of particle swarm optimization (PSO) and group method of data handling (GMDH). The PSO and GMDH are two well-known nonlinear methods of mathematical modeling. This novel method constructs a GMDH network model of a population of promising PSO solutions. The new PSO-GMDH hybrid im-plementation is then applied to modeling and prediction of practical datasets and its results are compared with the results obtained by GMDH-related algo-rithms. Results presented show that the proposed algorithm appears to perform reasonably well and hence can be applied to real-life prediction and modeling problems.

    Item Type: Book Chapter
    Additional Information: DOI: 10.1007/978-3-642-01530-4_5 Series ISSN: 1860-949X
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Q Science > QA Mathematics > QA76 Computer software
    Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
    Depositing User: Anuraganand Sharma
    Date Deposited: 04 Oct 2013 14:13
    Last Modified: 04 Oct 2013 14:13
    URI: http://repository.usp.ac.fj/id/eprint/6909
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