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Derating NichePSO

Dissertation (MSc)--University of Pretoria, 2007.

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Other Authors: Engelbrecht, Andries P.
Format: Thesis
Published: University of Pretoria 2013
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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 © University of Pretor
description Dissertation (MSc)--University of Pretoria, 2007.
format Thesis
id oai:repository.up.ac.za:2263/28766
institution University of Pretoria (South Africa)
last_indexed 2026-06-10T12:38:57.791Z
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
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source_str UPSpace — University of Pretoria Institutional Repository
spelling oai:repository.up.ac.za:2263/28766 Derating NichePSO Engelbrecht, Andries P. Naicker, Clive Multimodal Parallel Sequential Derating function Multiple UCTD Dissertation (MSc)--University of Pretoria, 2007. The search for multiple solutions is applicable to many fields (Engineering [54][67], Science [75][80][79][84][86], Economics [13][59], and others [51]). Multiple solutions allow for human judgement to select the best solution from a group of solutions that best match the search criteria. Finding multiple solutions to an optimisation problem has shown to be difficult to solve. Evolutionary computation (EC) and more recently Particle Swarm Optimisation (PSO) algorithms have been used in this field to locate and maintain multiple solutions with fair success. This thesis develops and empirically analyses a new method to find multiple solutions within a convoluted search space. The method is a hybrid of the NichePSO [14] and the sequential niche technique (SNT)[8]. The original SNT was developed using a Genetic Algorithm (GA). It included restrictions such as knowing or approximating the number of solutions that exist. A further pitfall of the SNT is that it introduces false optima after modifying the search space, thereby reducing the accuracy of the solutions. However, this can be resolved with a local search in the unmodified search space. Other sequential niching algorithms require that the search be repeated sequentially until all solutions are found without considering what was learned in previous iterations, resulting in a blind and wasteful search. The NichePSO has shown to be more accurate than GA based algorithms [14][15]. It does not require knowledge of the number of solutions in the search space prior to the search process. However, the NichePSO does not scale well for problems with many optima [16]. The method developed in this thesis, referred to as the derating NichePSO, combines SNT with the NichePSO. The main objective of the derating NichePSO is to eliminate the inaccuracy of SNT and to improve the scalability of the NichePSO. The derating NichePSO is compared to the NichePSO, deterministic crowding [23] and the original SNT using various multimodal functions. The performance of the derating NichePSO is analysed and it is shown that the derating NichePSO is more accurate than SNT and more scalable than the NichePSO. Computer Science MSc Unrestricted 2013-09-07T14:12:35Z 2007-11-08 2013-09-07T14:12:35Z 2007-04-29 2007-11-08 2007-10-17 Dissertation Naicker, C 2007, Derating NichePSO, MSc Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/28766> Pretoria http://hdl.handle.net/2263/28766 http://upetd.up.ac.za/thesis/available/etd-10172007-151316/ © University of Pretor application/pdf University of Pretoria
spellingShingle Multimodal
Parallel
Sequential
Derating function
Multiple
UCTD
Derating NichePSO
title Derating NichePSO
title_full Derating NichePSO
title_fullStr Derating NichePSO
title_full_unstemmed Derating NichePSO
title_short Derating NichePSO
title_sort derating nichepso
topic Multimodal
Parallel
Sequential
Derating function
Multiple
UCTD
url http://hdl.handle.net/2263/28766
http://upetd.up.ac.za/thesis/available/etd-10172007-151316/