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Tuning optimization algorithms under multiple objective function evaluation budgets
Thesis (PhD)--University of Pretoria, 2014
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2015
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| _version_ | 1867613713215782912 |
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| access_status_str | Open Access |
| author2 | Heyns, P.S. (Philippus Stephanus) |
| author_browse | Heyns, P.S. (Philippus Stephanus) |
| author_facet | Heyns, P.S. (Philippus Stephanus) |
| collection | Thesis |
| dc_rights_str_mv | © 2014 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, 2014 |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/43554 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:40:31.230Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2015 |
| publishDateRange | 2015 |
| publishDateSort | 2015 |
| 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:2263/43554 Tuning optimization algorithms under multiple objective function evaluation budgets Heyns, P.S. (Philippus Stephanus) antoine.dymond@gmail.com Kok, Schalk Dymond, Antoine Smith Dryden Performance of optimization algorithms Algorithm settings Benchmarking Multi-optimization problemobjective UCTD Thesis (PhD)--University of Pretoria, 2014 The performance of optimization algorithms is sensitive to both the optimization problem's numerical characteristics and the termination criteria of the algorithm. Given these considerations two tuning algorithms named tMOPSO and MOTA are proposed to assist optimization practitioners to nd algorithm settings which are approximate for the problem at hand. For a speci ed problem tMOPSO aims to determine multiple groups of control parameter values, each of which results in optimal performance at a di erent objective function evaluation budget. To achieve this, the control parameter tuning problem is formulated as a multi-objective optimization problem. Furthermore, tMOPSO uses a noise-handling strategy and control parameter value assessment procedure, which are specialized for tuning stochastic optimization algorithms. The principles upon which tMOPSO were designed are expanded into the context of many objective optimization, to create the MOTA tuning algorithm. MOTA tunes an optimization algorithm to multiple problems over a range of objective function evaluation budgets. To optimize the resulting many objective tuning problem, MOTA makes use of bi-objective decomposition. The last section of work entails an application of the tMOPSO and MOTA algorithms to benchmark optimization algorithms according to their tunability. Benchmarking via tunability is shown to be an effective approach for comparing optimization algorithms, where the various control parameter choices available to an optimization practitioner are included into the benchmarking process. gm2015 Mechanical and Aeronautical Engineering PhD Unrestricted 2015-02-05T10:22:42Z 2015-02-05T10:22:42Z 2014-09-05 2014 Thesis Dymond, ASD 2014, Tuning optimization algorithms under multiple objective function evaluation budgets, PhD Thesis, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/43554> D14/9/84 http://hdl.handle.net/2263/43554 en © 2014 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 | Performance of optimization algorithms Algorithm settings Benchmarking Multi-optimization problemobjective UCTD Tuning optimization algorithms under multiple objective function evaluation budgets |
| title | Tuning optimization algorithms under multiple objective function evaluation budgets |
| title_full | Tuning optimization algorithms under multiple objective function evaluation budgets |
| title_fullStr | Tuning optimization algorithms under multiple objective function evaluation budgets |
| title_full_unstemmed | Tuning optimization algorithms under multiple objective function evaluation budgets |
| title_short | Tuning optimization algorithms under multiple objective function evaluation budgets |
| title_sort | tuning optimization algorithms under multiple objective function evaluation budgets |
| topic | Performance of optimization algorithms Algorithm settings Benchmarking Multi-optimization problemobjective UCTD |
| url | http://hdl.handle.net/2263/43554 |