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A study of gradient based particle swarm optimisers
Dissertation (MSc)--University of Pretoria, 2010.
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| Format: | Thesis |
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University of Pretoria
2013
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| _version_ | 1867613458494652416 |
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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 | © 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. |
| description | Dissertation (MSc)--University of Pretoria, 2010. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/29927 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:36:28.181Z |
| 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:2263/29927 A study of gradient based particle swarm optimisers Engelbrecht, Andries P. danielbarla@gmail.com Barla-Szabo, Daniel Artificial intelligence Gradient methods Hybrid Particle swarm optimization (PSO) UCTD Dissertation (MSc)--University of Pretoria, 2010. Gradient-based optimisers are a natural way to solve optimisation problems, and have long been used for their efficacy in exploiting the search space. Particle swarm optimisers (PSOs), when using reasonable algorithm parameters, are considered to have good exploration characteristics. This thesis proposes a specific way of constructing hybrid gradient PSOs. Heterogeneous, hybrid gradient PSOs are constructed by allowing the gradient algorithm to optimise local best particles, while the PSO algorithm governs the behaviour of the rest of the swarm. This approach allows the distinct algorithms to concentrate on performing the separate tasks of exploration and exploitation. Two new PSOs, the Gradient Descent PSO, which combines the Gradient Descent and PSO algorithms, and the LeapFrog PSO, which combines the LeapFrog and PSO algorithms, are introduced. The GDPSO represents arguably the simplest hybrid gradient PSO possible, while the LeapFrog PSO incorporates the more sophisticated LFOP1(b) algorithm, exhibiting a heuristic algorithm design and dynamic time step adjustment mechanism. The strong tendency of these hybrids to prematurely converge is examined, and it is shown that by modifying algorithm parameters and delaying the introduction of gradient information, it is possible to retain strong exploration capabilities of the original PSO algorithm while also benefiting from the exploitation of the gradient algorithms. Computer Science unrestricted 2013-09-07T17:16:53Z 2011-05-10 2013-09-07T17:16:53Z 2010-05-19 2010 2010-11-29 Dissertation Barla-Szabo, D 2010, A study of gradient based particle swarm optimisers, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://hdl.handle.net/2263/29927 > C10/887/gm http://hdl.handle.net/2263/29927 http://upetd.up.ac.za/thesis/available/etd-11292010-143123/ © 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. application/pdf University of Pretoria |
| spellingShingle | Artificial intelligence Gradient methods Hybrid Particle swarm optimization (PSO) UCTD A study of gradient based particle swarm optimisers |
| title | A study of gradient based particle swarm optimisers |
| title_full | A study of gradient based particle swarm optimisers |
| title_fullStr | A study of gradient based particle swarm optimisers |
| title_full_unstemmed | A study of gradient based particle swarm optimisers |
| title_short | A study of gradient based particle swarm optimisers |
| title_sort | study of gradient based particle swarm optimisers |
| topic | Artificial intelligence Gradient methods Hybrid Particle swarm optimization (PSO) UCTD |
| url | http://hdl.handle.net/2263/29927 http://upetd.up.ac.za/thesis/available/etd-11292010-143123/ |