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  • The Effect of Population Density on the Performance of a Spatial Social Network Algorithm for Multi-objective Optimisation

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    Author(s)
    Lewis, Andrew
    Griffith University Author(s)
    Lewis, Andrew J.
    Year published
    2009
    Metadata
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    Abstract
    Particle Swarm Optimisation (PSO) is increasingly being applied to optimisation of multi-objective problems in engineering design and scientific investigation. This paper investigates the behaviour of a novel algorithm based on an extension of the concepts of spatial social networks using a model of the behaviour of locusts and crickets. In particular, observation of locust swarms suggests a specific dependence on population density for ordered behaviour. Computational experiments demonstrate that both the new, spatial, social network algorithm and a conventional MOPSO algorithm exhibit improved performance with ...
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    Particle Swarm Optimisation (PSO) is increasingly being applied to optimisation of multi-objective problems in engineering design and scientific investigation. This paper investigates the behaviour of a novel algorithm based on an extension of the concepts of spatial social networks using a model of the behaviour of locusts and crickets. In particular, observation of locust swarms suggests a specific dependence on population density for ordered behaviour. Computational experiments demonstrate that both the new, spatial, social network algorithm and a conventional MOPSO algorithm exhibit improved performance with increased swarm size and crowding. This observation may have particular significance for design of some forms of distributed PSO algorithms.
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    Conference Title
    2009 IEEE INTERNATIONAL SYMPOSIUM ON PARALLEL & DISTRIBUTED PROCESSING, VOLS 1-5
    Publisher URI
    http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5136864
    DOI
    https://doi.org/10.1109/IPDPS.2009.5161125
    Copyright Statement
    © 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    Subject
    Optimisation
    Publication URI
    http://hdl.handle.net/10072/25944
    Collection
    • Conference outputs

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