{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1724"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1724","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Enhancing meta-heuristic algorithms using center-based sampling at population level","abstract":"In recent years, center-based sampling has demonstrated impressive results to enhance the efficiency and effectiveness of meta-heuristic algorithms. The strategy of center-based sampling can be utilized at either the operation and/or population levels. Despite the overall efficiency of the center-based sampling in population-based algorithms, utilization at the operation level requires customizing the strategy for a specific algorithm which degrades the scheme’s generalization. This study proposes a center-based sampling at the population level, which is operation-independent and correspondingly can be embedded in any population-based optimization algorithm. In classic mutation and crossover operators, the number of parents involved is a few, causing ineffective exploration; however, the current proposed center-based sampling uses a multi-parent approach, which results in multiple center-based solutions. In this thesis, two proposed schemes, namely, 1) Clustering center-based sampling and 2) Average ranking center-based sampling, are applied to enhance population-based single- and multi-objective optimization algorithms, respectively, in order to enhance their exploration and exploitation capabilities. The conducted comprehensive center-based experiments are a novel strategy to enhance population based mechanistic algorithms. In order to assess the performance of proposed schemes, the proposed strategy is applied to single- and multi-objective optimization problems and experimented with CEC-2017 benchmark functions. The experimental outcomes confirm that the proposed clustering center-based and ranking center-based samplings have a crucial positive impact on convergence rate of various families of optimization algorithms.","abstract_html":"In recent years, center-based sampling has demonstrated impressive results to enhance the efficiency and effectiveness of meta-heuristic algorithms. The strategy of center-based sampling can be utilized at either the operation and/or population levels. Despite the overall efficiency of the center-based sampling in population-based algorithms, utilization at the operation level requires customizing the strategy for a specific algorithm which degrades the scheme’s generalization. This study proposes a center-based sampling at the population level, which is operation-independent and correspondingly can be embedded in any population-based optimization algorithm. In classic mutation and crossover operators, the number of parents involved is a few, causing ineffective exploration; however, the current proposed center-based sampling uses a multi-parent approach, which results in multiple center-based solutions. In this thesis, two proposed schemes, namely, 1) Clustering center-based sampling and 2) Average ranking center-based sampling, are applied to enhance population-based single- and multi-objective optimization algorithms, respectively, in order to enhance their exploration and exploitation capabilities. The conducted comprehensive center-based experiments are a novel strategy to enhance population based mechanistic algorithms. In order to assess the performance of proposed schemes, the proposed strategy is applied to single- and multi-objective optimization problems and experimented with CEC-2017 benchmark functions. The experimental outcomes confirm that the proposed clustering center-based and ranking center-based samplings have a crucial positive impact on convergence rate of various families of optimization algorithms.","abstract_has_math":false,"creators":["Khosrowshahli, Rasa"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Makrehchi, Masoud","Rahnamayan, Shahryar"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-11-01","date_published":"2023-11-01","updated_at":"2026-07-24T05:35:28Z","subjects":["Center-based sampling","Population-based algorithms","Single-objective optimization","Multi-objective optimization","Meta-heuristic algorithms"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1724","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Makrehchi, Masoud","Rahnamayan, Shahryar"]},{"key":"dc:creator","label":"Author","values":["Khosrowshahli, Rasa"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-01-23T20:10:16Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-01-23T20:10:16Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-11-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"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","Population-based algorithms","Single-objective optimization","Multi-objective optimization","Meta-heuristic algorithms"]}]},{"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/10155/1724"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In recent years, center-based sampling has demonstrated impressive results to enhance the efficiency and effectiveness of meta-heuristic algorithms. The strategy of center-based sampling can be utilized at either the operation and/or population levels. Despite the overall efficiency of the center-based sampling in population-based algorithms, utilization at the operation level requires customizing the strategy for a specific algorithm which degrades the scheme’s generalization. This study proposes a center-based sampling at the population level, which is operation-independent and correspondingly can be embedded in any population-based optimization algorithm. In classic mutation and crossover operators, the number of parents involved is a few, causing ineffective exploration; however, the current proposed center-based sampling uses a multi-parent approach, which results in multiple center-based solutions. In this thesis, two proposed schemes, namely, 1) Clustering center-based sampling and 2) Average ranking center-based sampling, are applied to enhance population-based single- and multi-objective optimization algorithms, respectively, in order to enhance their exploration and exploitation capabilities. The conducted comprehensive center-based experiments are a novel strategy to enhance population based mechanistic algorithms. In order to assess the performance of proposed schemes, the proposed strategy is applied to single- and multi-objective optimization problems and experimented with CEC-2017 benchmark functions. The experimental outcomes confirm that the proposed clustering center-based and ranking center-based samplings have a crucial positive impact on convergence rate of various families of optimization algorithms."]},{"key":"dc:title","label":"Title","values":["Enhancing meta-heuristic algorithms using center-based sampling at population level"]}]}],"canonical_facts":{"dc:contributor.advisor":["Makrehchi, Masoud","Rahnamayan, Shahryar"],"dc:creator":["Khosrowshahli, Rasa"],"dc:date.accessioned":["2024-01-23T20:10:16Z"],"dc:date.available":["2024-01-23T20:10:16Z"],"dc:date.issued":["2023-11-01"],"dc:description.abstract":["In recent years, center-based sampling has demonstrated impressive results to enhance the efficiency and effectiveness of meta-heuristic algorithms. The strategy of center-based sampling can be utilized at either the operation and/or population levels. Despite the overall efficiency of the center-based sampling in population-based algorithms, utilization at the operation level requires customizing the strategy for a specific algorithm which degrades the scheme’s generalization. This study proposes a center-based sampling at the population level, which is operation-independent and correspondingly can be embedded in any population-based optimization algorithm. In classic mutation and crossover operators, the number of parents involved is a few, causing ineffective exploration; however, the current proposed center-based sampling uses a multi-parent approach, which results in multiple center-based solutions. In this thesis, two proposed schemes, namely, 1) Clustering center-based sampling and 2) Average ranking center-based sampling, are applied to enhance population-based single- and multi-objective optimization algorithms, respectively, in order to enhance their exploration and exploitation capabilities. The conducted comprehensive center-based experiments are a novel strategy to enhance population based mechanistic algorithms. In order to assess the performance of proposed schemes, the proposed strategy is applied to single- and multi-objective optimization problems and experimented with CEC-2017 benchmark functions. The experimental outcomes confirm that the proposed clustering center-based and ranking center-based samplings have a crucial positive impact on convergence rate of various families of optimization algorithms."],"dc:identifier.uri":["https://hdl.handle.net/10155/1724"],"dc:language.iso":["en"],"dc:subject":["Center-based sampling","Population-based algorithms","Single-objective optimization","Multi-objective optimization","Meta-heuristic algorithms"],"dc:title":["Enhancing meta-heuristic algorithms using center-based sampling at population level"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:28Z"}