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Driving dynamic multi-objective optimizations constrained by decision-makers' preferences
Dissertation (MSc (Computer Science)) -- University of Pretoria, 2019.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2019
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| _version_ | 1867613454899085312 |
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| access_status_str | Open Access |
| author2 | Marde, Helbig |
| author_browse | Marde, Helbig |
| author_facet | Marde, Helbig |
| collection | Thesis |
| dc_rights_str_mv | © 2019 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 (Computer Science)) -- University of Pretoria, 2019. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/70790 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:36:24.683Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| 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:2263/70790 Driving dynamic multi-objective optimizations constrained by decision-makers' preferences Marde, Helbig adekunleadekoya@gmail.com Adekoya, Adekunle Rotimi UCTD Constrained optimization Dynamic multi-objective optimization Decisionmaker preference incorporation Differential evolution Evolutionary and nature-inspired computation Benchmark functions and performance measures Engineering, built environment and information technology theses SDG-08 Engineering, built environment and information technology theses SDG-09 Engineering, built environment and information technology theses SDG-12 Dissertation (MSc (Computer Science)) -- University of Pretoria, 2019. Dynamic multi-objective optimization problems (DMOOPs) are an interesting and a relatively complex class of optimization problems where elements of the problems, such as objective functions and/or constraints, change with time. These problems are characterized with at least two objective functions in con ict with one another. Sometimes, human decision-makers seek to in uence ways (by restricting the search to a specific region of the Pareto-optimal Front (POF)) in which algorithms that optimize these problems behave by incorporating personal preferences into the optimization process. This dissertation proposes approaches that enable decision-makers to in uence the optimization process with their preferences. The decision-makers' imparted preferences force a reformulation of the optimization problems as constrained problems, where the constraints are defined in the objective space. Consequently, the constrained problems are then solved using variations of constraint handling techniques, such as penalization of infeasible solutions and the restriction of the search to the feasible region. The proposed algorithmic approaches' performance are compared using standard performance measures for dynamic multi-objective optimization (DMOO) and newly proposed measures. The proposed measures estimate how well an algorithm is able to find solutions in the objective space that best re ect the decision-maker's preferences and the paretooptimality goal of DMOO. This dissertation also proposes a new di erential evolution algorithm, called dynamic di erential evolution vector-evaluated non-dominated sorting (2DEVENS). 2DEVENS combines elements of the dynamic non-dominated sort genetic algorithm version II (DNSGA-II) and the dynamic vector-evaluated particle swarm optimization (DVEPSO) algorithm to drive the search for solutions. The proposed 2DEVENS algorithm compared favorably with other nature-inspired algorithms that were used in the studies carried out for this dissertation. The proposed approaches used in incorporating decision-makers' preferences in the optimization process also demonstrated good results. bs2026 Computer Science MSc (Computer Science) Unrestricted SDG-08: Decent work and economic growth SDG-09: Industry, innovation and infrastructure SDG-12: Responsible consumption and production 2019-07-24T14:31:17Z 2019-07-24T14:31:17Z 2019 2019 Dissertation * S2019 http://hdl.handle.net/2263/70790 en © 2019 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 | UCTD Constrained optimization Dynamic multi-objective optimization Decisionmaker preference incorporation Differential evolution Evolutionary and nature-inspired computation Benchmark functions and performance measures Engineering, built environment and information technology theses SDG-08 Engineering, built environment and information technology theses SDG-09 Engineering, built environment and information technology theses SDG-12 Driving dynamic multi-objective optimizations constrained by decision-makers' preferences |
| title | Driving dynamic multi-objective optimizations constrained by decision-makers' preferences |
| title_full | Driving dynamic multi-objective optimizations constrained by decision-makers' preferences |
| title_fullStr | Driving dynamic multi-objective optimizations constrained by decision-makers' preferences |
| title_full_unstemmed | Driving dynamic multi-objective optimizations constrained by decision-makers' preferences |
| title_short | Driving dynamic multi-objective optimizations constrained by decision-makers' preferences |
| title_sort | driving dynamic multi objective optimizations constrained by decision makers preferences |
| topic | UCTD Constrained optimization Dynamic multi-objective optimization Decisionmaker preference incorporation Differential evolution Evolutionary and nature-inspired computation Benchmark functions and performance measures Engineering, built environment and information technology theses SDG-08 Engineering, built environment and information technology theses SDG-09 Engineering, built environment and information technology theses SDG-12 |
| url | http://hdl.handle.net/2263/70790 |