Title page for ETD etd-07222010-111453


Document Type Doctoral Thesis
Author Schoeman, Isabella Lodewina
Email lona.schoeman@gmail.com
URN etd-07222010-111453
Document Title Niching in particle swarm optimization
Degree PhD
Department Computer Science
Supervisor
Advisor Name Title
Prof A P Engelbrecht Committee Chair
Keywords
  • speciation
  • particle swarm optimization
  • Niching
  • dynamic
Date 2010-07-22
Availability unrestricted
Abstract
Optimization forms an intrinsic part of the design and implementation of modern systems, such as industrial systems, communication networks, and the configuration of electric or electronic components. Population-based single-solution optimization algorithms such as Particle Swarm Optimization (PSO) have been shown to perform well when a number of optimal or suboptimal solutions exist. However, some problems require algorithms that locate all or most of these optimal and suboptimal solutions. Such algorithms are known as niching or speciation algorithms.

Several techniques have been proposed to extend the PSO paradigm so that multiple optima can be located and maintained within a convoluted search space. A significant number of these implementations are subswarm-based, that is, portions of the swarm are optimized separately. Niches are formed to contain these subswarms, a process that often requires user-specified parameters, as the sizes and placing of the niches are unknown. This thesis presents a new niching technique that uses the vector dot product of the social and cognitive direction vectors to determine niche boundaries. Thus, a separate niche radius is calculated for each niche, a process that requires minimal knowledge of the search space. This strategy differs from other techniques using niche radii where a niche radius is either required to be set in advance, or calculated from the distances between particles.

The development of the algorithm is traced and tested extensively using synthetic benchmark functions. Empirical results are reported using a variety of settings. An analysis of the algorithm is presented as well as a scalability study. The performance of the algorithm is also compared to that of two other well-known PSO niching algorithms. To conclude, the vector-based PSO is extended to locate and track multiple optima in dynamic environments.

2010 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:

Schoeman, IL 2010, Niching in particle swarm optimization, PhD thesis, University of Pretoria, Pretoria, viewed yymmdd < http://upetd.up.ac.za/thesis/available/etd-07222010-111453/ >

B10/537/ag

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