{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1253"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1253","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Toward enhancing metaheuristic optimization algorithms using center-based sampling strategies for solving single- and multi- objective large-scale problems","abstract":"Over the last decade, metaheuristic algorithms have become well-established approaches utilized for solving complex real-world optimization problems. Most metaheuristic algorithms have used stochastic strategies in their initialization as well as during the new candidate solution generation process where there is no a priori knowledge about the solution, which is a common assumption for any black-box optimization problem. In recent years, researchers have introduced a new concept called center-based sampling that can be used in any search component of the optimization process, but so far, it has mainly been utilized for population initialization. This concept clarifies that in a search space, the center point has a higher probability value to be closer to an unknown solution compared to a uniformly generated random point, especially when the dimension increases. Thus, this novel concept helps the optimizer to find a better solution efficiently. In this thesis, a comprehensive study has been conducted on the effect of center-based sampling to solve an optimization problem using three different levels of investigation. These levels are as follows: 1) no specific algorithm and no specific landscape (i.e., Monte-Carlo-based simulation); 2) a specific landscape but no specific algorithm (random search vs. center-based random search), and finally, 3) a specific algorithm and specific landscape (proposing three different schemes for using center-based sampling for solving Large-scale Global Optimization (LSGO) problems). Also, a center-based sampling for multi-objective optimization is proposed. Furthermore, in this thesis, I seek to investigate the properties and capabilities of center-based sampling during optimization, which can be extended to utilize it in machine learning techniques, as well. The proposed methods are evaluated on discrete and continuous Large-scale Global Optimization (LSGO) benchmark functions. The experimental results confirm that center based sampling has a crucial impact on improving the convergence rate of optimization/search algorithms when solving high-dimensional optimization problems.","abstract_html":"Over the last decade, metaheuristic algorithms have become well-established approaches utilized for solving complex real-world optimization problems. Most metaheuristic algorithms have used stochastic strategies in their initialization as well as during the new candidate solution generation process where there is no a priori knowledge about the solution, which is a common assumption for any black-box optimization problem. In recent years, researchers have introduced a new concept called center-based sampling that can be used in any search component of the optimization process, but so far, it has mainly been utilized for population initialization. This concept clarifies that in a search space, the center point has a higher probability value to be closer to an unknown solution compared to a uniformly generated random point, especially when the dimension increases. Thus, this novel concept helps the optimizer to find a better solution efficiently. In this thesis, a comprehensive study has been conducted on the effect of center-based sampling to solve an optimization problem using three different levels of investigation. These levels are as follows: 1) no specific algorithm and no specific landscape (i.e., Monte-Carlo-based simulation); 2) a specific landscape but no specific algorithm (random search vs. center-based random search), and finally, 3) a specific algorithm and specific landscape (proposing three different schemes for using center-based sampling for solving Large-scale Global Optimization (LSGO) problems). Also, a center-based sampling for multi-objective optimization is proposed. Furthermore, in this thesis, I seek to investigate the properties and capabilities of center-based sampling during optimization, which can be extended to utilize it in machine learning techniques, as well. The proposed methods are evaluated on discrete and continuous Large-scale Global Optimization (LSGO) benchmark functions. The experimental results confirm that center based sampling has a crucial impact on improving the convergence rate of optimization/search algorithms when solving high-dimensional optimization problems.","abstract_has_math":false,"creators":["Hiba, Hanan"],"institution":"University of Ontario Institute of Technology","degree_name":"Doctor of Philosophy (PhD)","degree_level":null,"degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Rahnamayan, Shahryar"],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-12-01","date_published":"2020-12-01","updated_at":"2026-07-24T05:35:38Z","subjects":["Center-based sampling","Large-scale optimization","Monte-Carlo simulation","Differential evolution","High-dimensional optimization"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1253","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Rahnamayan, Shahryar"]},{"key":"dc:creator","label":"Author","values":["Hiba, Hanan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-02-26T16:48:45Z","2022-03-29T18:10:00Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-02-26T16:48:45Z","2022-03-29T18:10:00Z"]},{"key":"dc:date.issued","label":"Date","values":["2020-12-01"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Center-based sampling","Large-scale optimization","Monte-Carlo simulation","Differential evolution","High-dimensional optimization"]}]},{"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":["https://hdl.handle.net/10155/1253"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Over the last decade, metaheuristic algorithms have become well-established approaches utilized for solving complex real-world optimization problems. Most metaheuristic algorithms have used stochastic strategies in their initialization as well as during the new candidate solution generation process where there is no a priori knowledge about the solution, which is a common assumption for any black-box optimization problem. In recent years, researchers have introduced a new concept called center-based sampling that can be used in any search component of the optimization process, but so far, it has mainly been utilized for population initialization. This concept clarifies that in a search space, the center point has a higher probability value to be closer to an unknown solution compared to a uniformly generated random point, especially when the dimension increases. Thus, this novel concept helps the optimizer to find a better solution efficiently. In this thesis, a comprehensive study has been conducted on the effect of center-based sampling to solve an optimization problem using three different levels of investigation. These levels are as follows: 1) no specific algorithm and no specific landscape (i.e., Monte-Carlo-based simulation); 2) a specific landscape but no specific algorithm (random search vs. center-based random search), and finally, 3) a specific algorithm and specific landscape (proposing three different schemes for using center-based sampling for solving Large-scale Global Optimization (LSGO) problems). Also, a center-based sampling for multi-objective optimization is proposed. Furthermore, in this thesis, I seek to investigate the properties and capabilities of center-based sampling during optimization, which can be extended to utilize it in machine learning techniques, as well. The proposed methods are evaluated on discrete and continuous Large-scale Global Optimization (LSGO) benchmark functions. The experimental results confirm that center based sampling has a crucial impact on improving the convergence rate of optimization/search algorithms when solving high-dimensional optimization problems."]},{"key":"dc:title","label":"Title","values":["Toward enhancing metaheuristic optimization algorithms using center-based sampling strategies for solving single- and multi- objective large-scale problems"]}]}],"canonical_facts":{"dc:contributor.advisor":["Rahnamayan, Shahryar"],"dc:creator":["Hiba, Hanan"],"dc:date.accessioned":["2021-02-26T16:48:45Z","2022-03-29T18:10:00Z"],"dc:date.available":["2021-02-26T16:48:45Z","2022-03-29T18:10:00Z"],"dc:date.issued":["2020-12-01"],"dc:description.abstract":["Over the last decade, metaheuristic algorithms have become well-established approaches utilized for solving complex real-world optimization problems. Most metaheuristic algorithms have used stochastic strategies in their initialization as well as during the new candidate solution generation process where there is no a priori knowledge about the solution, which is a common assumption for any black-box optimization problem. In recent years, researchers have introduced a new concept called center-based sampling that can be used in any search component of the optimization process, but so far, it has mainly been utilized for population initialization. This concept clarifies that in a search space, the center point has a higher probability value to be closer to an unknown solution compared to a uniformly generated random point, especially when the dimension increases. Thus, this novel concept helps the optimizer to find a better solution efficiently. In this thesis, a comprehensive study has been conducted on the effect of center-based sampling to solve an optimization problem using three different levels of investigation. These levels are as follows: 1) no specific algorithm and no specific landscape (i.e., Monte-Carlo-based simulation); 2) a specific landscape but no specific algorithm (random search vs. center-based random search), and finally, 3) a specific algorithm and specific landscape (proposing three different schemes for using center-based sampling for solving Large-scale Global Optimization (LSGO) problems). Also, a center-based sampling for multi-objective optimization is proposed. Furthermore, in this thesis, I seek to investigate the properties and capabilities of center-based sampling during optimization, which can be extended to utilize it in machine learning techniques, as well. The proposed methods are evaluated on discrete and continuous Large-scale Global Optimization (LSGO) benchmark functions. The experimental results confirm that center based sampling has a crucial impact on improving the convergence rate of optimization/search algorithms when solving high-dimensional optimization problems."],"dc:identifier.uri":["https://hdl.handle.net/10155/1253"],"dc:language.iso":["en"],"dc:subject":["Center-based sampling","Large-scale optimization","Monte-Carlo simulation","Differential evolution","High-dimensional optimization"],"dc:title":["Toward enhancing metaheuristic optimization algorithms using center-based sampling strategies for solving single- and multi- objective large-scale problems"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_name":["Doctor of Philosophy (PhD)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:38Z"}