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    Particle swarms and nonextensive statistics for nonlinear optimisation

    Anastasiadis, A.D. and Magoulas, George D. (2008) Particle swarms and nonextensive statistics for nonlinear optimisation. The Open Cybernetics & Systemics Journal 2 , pp. 173-179. ISSN 1874-110X.

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    Abstract

    Particle swarm methods are inspired from the dynamics of social interaction and employ information sharing to seek solutions to difficult optimisation problems. In this paper we introduce an approach that combines ideas from particle swarm optimisation (PSO) and the theory of nonextensive statistical mechanics. We develop two algorithms that adopt this approach and conduct an experimental study using benchmark functions to investigate their effectiveness in nonlinear optimisation. Results appear to be promising, as the tested algorithms outperform in most cases the standard PSO and other, recently proposed, PSO variants.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): Particle swarm optimiser, global search methods, statistical mechanics, nonextensive statistical mechanics, nonlinear optimisation
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centre: Birkbeck Knowledge Lab
    Depositing User: Administrator
    Date Deposited: 02 Feb 2011 12:57
    Last Modified: 02 Dec 2016 13:23
    URI: http://eprints.bbk.ac.uk/id/eprint/1878

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