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 ISBN

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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  • Cite this

    Gheitanchi, S., Ali, F., & Stipidis, E. (2008). Trained particle swarm optimization for ad-hoc collaborative computing networks. In AISB 2008 Convention: Communication, Interaction and Social Intelligence - Proceedings of the AISB 2008 Symposium on Swarm Intelligence Algorithms and Applications (pp. 7-11)