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An Analysis of Particle Swarm Optimizers
Thesis (PhD)--University of Pretoria, 2007.
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University of Pretoria
2013
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| _version_ | 1867613725094051840 |
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| access_status_str | Open Access |
| author2 | Engelbrecht, Andries P. |
| author_browse | Engelbrecht, Andries P. |
| author_facet | Engelbrecht, Andries P. |
| collection | Thesis |
| dc_rights_str_mv | © 2002, 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. |
| description | Thesis (PhD)--University of Pretoria, 2007. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/24297 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:40:42.021Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2013 |
| publishDateRange | 2013 |
| publishDateSort | 2013 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/24297 An Analysis of Particle Swarm Optimizers Engelbrecht, Andries P. fvdbergh@gmail.com Van den Bergh, Frans Particle swarm optimization (PSO) Mathematical optimization Neural network training UCTD Thesis (PhD)--University of Pretoria, 2007. Many scientific, engineering and economic problems involve the optimisation of a set of parameters. These problems include examples like minimising the losses in a power grid by finding the optimal configuration of the components, or training a neural network to recognise images of people's faces. Numerous optimisation algorithms have been proposed to solve these problems, with varying degrees of success. The Particle Swarm Optimiser (PSO) is a relatively new technique that has been empirically shown to perform well on many of these optimisation problems. This thesis presents a theoretical model that can be used to describe the long-term behaviour of the algorithm. An enhanced version of the Particle Swarm Optimiser is constructed and shown to have guaranteed convergence on local minima. This algorithm is extended further, resulting in an algorithm with guaranteed convergence on global minima. A model for constructing cooperative PSO algorithms is developed, resulting in the introduction of two new PSO-based algorithms. Empirical results are presented to support the theoretical properties predicted by the various models, using synthetic benchmark functions to investigate specific properties. The various PSO-based algorithms are then applied to the task of training neural networks, corroborating the results obtained on the synthetic benchmark functions. Computer Science Unrestricted 2013-09-06T17:05:30Z 2006-05-19 2013-09-06T17:05:30Z 2002-04-14 2007-05-19 2006-05-03 Thesis Van den Bergh, F 2002, An Analysis of Particle Swarm Optimizers, PhD(Computer thesis, University of Pretoria, Pretoria, viewed yymmdd < http://hdl.handle.net/2263/24297 > http://hdl.handle.net/2263/24297 http://upetd.up.ac.za/thesis/available/etd-05032006-160549/ © 2002, 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. application/pdf University of Pretoria |
| spellingShingle | Particle swarm optimization (PSO) Mathematical optimization Neural network training UCTD An Analysis of Particle Swarm Optimizers |
| title | An Analysis of Particle Swarm Optimizers |
| title_full | An Analysis of Particle Swarm Optimizers |
| title_fullStr | An Analysis of Particle Swarm Optimizers |
| title_full_unstemmed | An Analysis of Particle Swarm Optimizers |
| title_short | An Analysis of Particle Swarm Optimizers |
| title_sort | analysis of particle swarm optimizers |
| topic | Particle swarm optimization (PSO) Mathematical optimization Neural network training UCTD |
| url | http://hdl.handle.net/2263/24297 http://upetd.up.ac.za/thesis/available/etd-05032006-160549/ |