Trained particle swarm optimization for ad-hoc collaborative computing networks

Shahin Gheitanchi, Falah Ali, Elias Stipidis

    Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBNpeer-review

    Abstract

    Distributed processing is an essential part of collaborative computing techniques over ad-hoc networks. In this paper, a generalized particle swarm optimization (PSO) model for communication networks is introduced. A modified version of PSO, called trained PSO (TPSO), consisting of distributed particles that are adapted to reduce traffic and computational overhead of the optimization process is proposed. The TPSO technique is used to find the node with the highest processing load in an ad-hoc collaborative computing system. The simulation results show that the TPSO algorithm significantly reduces the traffic overhead, computation complexity and convergence time of particles, in comparison to the PSO.

    Original languageEnglish
    Title of host publicationAISB 2008 Convention
    Subtitle of host publicationCommunication, Interaction and Social Intelligence - Proceedings of the AISB 2008 Symposium on Swarm Intelligence Algorithms and Applications
    Pages7-11
    Number of pages5
    Publication statusPublished - 1 Dec 2008
    EventAISB 2008 Symposium on Swarm Intelligence Algorithms and Applications - Aberdeen, United Kingdom
    Duration: 1 Apr 20084 Apr 2008

    Conference

    ConferenceAISB 2008 Symposium on Swarm Intelligence Algorithms and Applications
    CountryUnited Kingdom
    CityAberdeen
    Period1/04/084/04/08

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