University of Sussex
Browse

File(s) not publicly available

Trained Particle Swarm Optimization for Ad-Hoc Collaborative Computing Networks

presentation
posted on 2023-06-08, 06:35 authored by Shahin Gheitanchi, Falah AliFalah Ali, Elias Stipidis
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. 1 INTRODUCTION

History

Publication status

  • Published

Pages

5.0

Presentation Type

  • paper

Event name

AISB 2008 Convention, Symposium on Swarm Intelligence Algorithms and Applications

Event location

Aberdeen, UK

Event type

conference

Department affiliated with

  • Engineering and Design Publications

Full text available

  • No

Peer reviewed?

  • Yes

Legacy Posted Date

2012-02-06

Usage metrics

    University of Sussex (Publications)

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC