Title page for ETD etd-06262012-124432


Document Type Master's Dissertation
Author Duhain, Julien Georges Omer Louis
Email julien.duhain@gmail.com
URN etd-06262012-124432
Document Title Particle swarm optimisation in dynamically changing environments - an empirical study
Degree MSc
Department Computer Science
Supervisor
Advisor Name Title
Prof A P Engelbrecht Supervisor
Keywords
  • atomic PSO
  • charged PSO
  • self-adapting multi-swarm
  • re-evaluating PSO
  • particle swarm optimisation (PSO)
  • dynamically changing environment
  • quantum swarm optimisation
  • reinitialising PSO
  • computational intelligence
  • multi-swarm
Date 2012-04-19
Availability unrestricted
Abstract
Real-world optimisation problems often are of a dynamic nature. Recently, much research has been done to apply particle swarm optimisation (PSO) to dynamic environments (DE). However, these research efforts generally focused on optimising one variation of the PSO algorithm for one type of DE. The aim of this work is to develop a more comprehensive view of PSO for DEs. This thesis studies different schemes of characterising and taxonomising DEs, performance measures used to quantify the performance of optimisation algorithms applied to DEs, various adaptations of PSO to apply PSO to DEs, and the effectiveness of these approaches on different DE types.

The standard PSO algorithm has shown limitations when applied to DEs. To overcome these limitations, the standard PSO can be modi ed using personal best reevaluation, change detection and response, diversity maintenance, or swarm sub-division and parallel tracking of optima. To investigate the strengths and weaknesses of these approaches, a representative sample of algorithms, namely, the standard PSO, re-evaluating PSO, reinitialising PSO, atomic PSO (APSO), quantum swarm optimisation (QSO), multi-swarm, and self-adapting multi-swarm (SAMS), are empirically analysed. These algorithms are analysed on a range of DE test cases, and their ability to detect and track optima are evaluated using performance measures designed for DEs. The experiments show that QSO, multi-swarm and reinitialising PSO provide the best results. However, the most effective approach to use depends on the dimensionality, modality and type of the DEs, as well as on the objective of the algorithm. A number of observations are also made regarding the behaviour of the swarms, and the influence of certain control parameters of the algorithms evaluated.

Copyright 2011, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria

Please cite as follows:

Duhain, JGOL 2011, Particle swarm optimisation in dynamically changing environments - an empirical study, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://upetd.up.ac.za/thesis/available/etd-06262012-124432 / >

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