{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/42760"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/42760","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Design problem optimization with multi-objective evolutionary algorithms","abstract":"Complex design challenges involve conflicting objectives and require robust optimization techniques. They commonly arise in engineering, building design, robotics, drug design, and energy systems, among others, where balancing competing criteria is essential. Sunshade optimization is also a complex design problem as it has many conflicting objectives. Sunshades significantly influence a building's thermal performance, daylight quality, occupant comfort, and energy usage. However, traditional sunshade designs typically focus on a limited set of objectives, often ignoring broader considerations such as cost efficiency and outside-view obstruction. This thesis addresses that gap by implementing and comparing two advanced multi-objective evolutionary algorithms—Multi-Objective Covariance Matrix Adaptation Evolution Strategy (MOCMA-ES) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II)—to optimize sunshades across five key objectives: thermal comfort, energy consumption, Useful Daylight Illuminance (UDI), cost, and outside-view obstruction. A single-room office model was used as a test bed, with parameterized sunshades simulated through Honeybee, EnergyPlus, and Radiance. Experiments were conducted in four distinct climate zones—Cape Town (moderate), Nairobi (hot), Colombo (hothumid), and Oslo (cold)—to ensure broad applicability. Both algorithms consistently outperformed traditional, manually designed sunshades in reducing thermal discomfort and energy usage while also improving UDI. Gains in cost and view preservation were more modest, primarily because minimal overhang sunshades can already be inexpensive and unobtrusive. Statistical tests indicated no systematic performance advantage of one algorithm over the other; NSGA-II tended to produce larger Pareto fronts, whereas MOCMA-ES explored a broader range of objective values. The main contribution of this research is the use of two advanced multi-objective evolutionary algorithms to optimize sunshade designs based on five key objectives, tested in four climate zones representing both the northern and southern hemispheres, as well as regions below and above the equator, demonstrating clear advantages over traditional, manually designed sunshades in achieving a balanced trade-off among competing performance criteria.","abstract_html":"Complex design challenges involve conflicting objectives and require robust optimization techniques. They commonly arise in engineering, building design, robotics, drug design, and energy systems, among others, where balancing competing criteria is essential. Sunshade optimization is also a complex design problem as it has many conflicting objectives. Sunshades significantly influence a building's thermal performance, daylight quality, occupant comfort, and energy usage. However, traditional sunshade designs typically focus on a limited set of objectives, often ignoring broader considerations such as cost efficiency and outside-view obstruction. This thesis addresses that gap by implementing and comparing two advanced multi-objective evolutionary algorithms—Multi-Objective Covariance Matrix Adaptation Evolution Strategy (MOCMA-ES) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II)—to optimize sunshades across five key objectives: thermal comfort, energy consumption, Useful Daylight Illuminance (UDI), cost, and outside-view obstruction. A single-room office model was used as a test bed, with parameterized sunshades simulated through Honeybee, EnergyPlus, and Radiance. Experiments were conducted in four distinct climate zones—Cape Town (moderate), Nairobi (hot), Colombo (hothumid), and Oslo (cold)—to ensure broad applicability. Both algorithms consistently outperformed traditional, manually designed sunshades in reducing thermal discomfort and energy usage while also improving UDI. Gains in cost and view preservation were more modest, primarily because minimal overhang sunshades can already be inexpensive and unobtrusive. Statistical tests indicated no systematic performance advantage of one algorithm over the other; NSGA-II tended to produce larger Pareto fronts, whereas MOCMA-ES explored a broader range of objective values. The main contribution of this research is the use of two advanced multi-objective evolutionary algorithms to optimize sunshade designs based on five key objectives, tested in four climate zones representing both the northern and southern hemispheres, as well as regions below and above the equator, demonstrating clear advantages over traditional, manually designed sunshades in achieving a balanced trade-off among competing performance criteria.","abstract_has_math":false,"creators":["Toma, Farzana Haque"],"institution":"Department of Computer Science","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Nitschke, Geoff Stuart"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-22T22:23:05Z","subjects":["Matrix Adaptation Evolution Strategy","Non-Dominated Sorting Genetic Algorithm"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/42760","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Nitschke, Geoff Stuart"]},{"key":"dc:creator","label":"Author","values":["Toma, Farzana Haque"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-01-29T13:24:38Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-01-29T13:24:38Z"]},{"key":"dc:date.issued","label":"Date","values":["2025"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Computer Science"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cape Town"]},{"key":"dc:type","label":"Dc Type","values":["Thesis / Dissertation"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters","MSc"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Matrix Adaptation Evolution Strategy","Non-Dominated Sorting Genetic Algorithm"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/42760"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Complex design challenges involve conflicting objectives and require robust optimization techniques. They commonly arise in engineering, building design, robotics, drug design, and energy systems, among others, where balancing competing criteria is essential. Sunshade optimization is also a complex design problem as it has many conflicting objectives. Sunshades significantly influence a building's thermal performance, daylight quality, occupant comfort, and energy usage. However, traditional sunshade designs typically focus on a limited set of objectives, often ignoring broader considerations such as cost efficiency and outside-view obstruction. This thesis addresses that gap by implementing and comparing two advanced multi-objective evolutionary algorithms—Multi-Objective Covariance Matrix Adaptation Evolution Strategy (MOCMA-ES) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II)—to optimize sunshades across five key objectives: thermal comfort, energy consumption, Useful Daylight Illuminance (UDI), cost, and outside-view obstruction. A single-room office model was used as a test bed, with parameterized sunshades simulated through Honeybee, EnergyPlus, and Radiance. Experiments were conducted in four distinct climate zones—Cape Town (moderate), Nairobi (hot), Colombo (hothumid), and Oslo (cold)—to ensure broad applicability. Both algorithms consistently outperformed traditional, manually designed sunshades in reducing thermal discomfort and energy usage while also improving UDI. Gains in cost and view preservation were more modest, primarily because minimal overhang sunshades can already be inexpensive and unobtrusive. Statistical tests indicated no systematic performance advantage of one algorithm over the other; NSGA-II tended to produce larger Pareto fronts, whereas MOCMA-ES explored a broader range of objective values. The main contribution of this research is the use of two advanced multi-objective evolutionary algorithms to optimize sunshade designs based on five key objectives, tested in four climate zones representing both the northern and southern hemispheres, as well as regions below and above the equator, demonstrating clear advantages over traditional, manually designed sunshades in achieving a balanced trade-off among competing performance criteria."]},{"key":"dc:title","label":"Title","values":["Design problem optimization with multi-objective evolutionary algorithms"]}]}],"canonical_facts":{"dc:contributor.advisor":["Nitschke, Geoff Stuart"],"dc:creator":["Toma, Farzana Haque"],"dc:date.accessioned":["2026-01-29T13:24:38Z"],"dc:date.available":["2026-01-29T13:24:38Z"],"dc:date.issued":["2025"],"dc:description.abstract":["Complex design challenges involve conflicting objectives and require robust optimization techniques. They commonly arise in engineering, building design, robotics, drug design, and energy systems, among others, where balancing competing criteria is essential. Sunshade optimization is also a complex design problem as it has many conflicting objectives. Sunshades significantly influence a building's thermal performance, daylight quality, occupant comfort, and energy usage. However, traditional sunshade designs typically focus on a limited set of objectives, often ignoring broader considerations such as cost efficiency and outside-view obstruction. This thesis addresses that gap by implementing and comparing two advanced multi-objective evolutionary algorithms—Multi-Objective Covariance Matrix Adaptation Evolution Strategy (MOCMA-ES) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II)—to optimize sunshades across five key objectives: thermal comfort, energy consumption, Useful Daylight Illuminance (UDI), cost, and outside-view obstruction. A single-room office model was used as a test bed, with parameterized sunshades simulated through Honeybee, EnergyPlus, and Radiance. Experiments were conducted in four distinct climate zones—Cape Town (moderate), Nairobi (hot), Colombo (hothumid), and Oslo (cold)—to ensure broad applicability. Both algorithms consistently outperformed traditional, manually designed sunshades in reducing thermal discomfort and energy usage while also improving UDI. Gains in cost and view preservation were more modest, primarily because minimal overhang sunshades can already be inexpensive and unobtrusive. Statistical tests indicated no systematic performance advantage of one algorithm over the other; NSGA-II tended to produce larger Pareto fronts, whereas MOCMA-ES explored a broader range of objective values. The main contribution of this research is the use of two advanced multi-objective evolutionary algorithms to optimize sunshade designs based on five key objectives, tested in four climate zones representing both the northern and southern hemispheres, as well as regions below and above the equator, demonstrating clear advantages over traditional, manually designed sunshades in achieving a balanced trade-off among competing performance criteria."],"dc:identifier.uri":["http://hdl.handle.net/11427/42760"],"dc:language.iso":["en"],"dc:publisher.department":["Department of Computer Science"],"dc:publisher.institution":["University of Cape Town"],"dc:subject":["Matrix Adaptation Evolution Strategy","Non-Dominated Sorting Genetic Algorithm"],"dc:title":["Design problem optimization with multi-objective evolutionary algorithms"],"dc:type":["Thesis / Dissertation"],"dc:type.qualificationlevel":["Masters","MSc"]},"updated_at":"2026-07-22T22:23:05Z"}