{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/17"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/17","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Optimization-based mechanism synthesis using multi-objective parallel asynchronous particle swarm optimization","abstract":"A distributed variant of multi-objective particle swarm optimization (MOPSO) called multi-objective parallel asynchronous particle swarm optimization (MOPAPSO) is presented, and the effects of distribution of objective function calculations to slave processors on the results and performance are investigated and employed for the synthesis of Grashof mechanisms. By using a formal multi-objective handling scheme based on Pareto dominance criteria, the need to pre-weight competing systemic objective functions is removed and the optimal solution for a design problem can be selected from a front of candidates after the parameter optimization has been completed. MOPAPSO's ability to match MOPSO's results using parallelization for improved performance is presented. Results for both four and ve bar mechanism synthesis examples are shown.","abstract_html":"A distributed variant of multi-objective particle swarm optimization (MOPSO) called multi-objective parallel asynchronous particle swarm optimization (MOPAPSO) is presented, and the effects of distribution of objective function calculations to slave processors on the results and performance are investigated and employed for the synthesis of Grashof mechanisms. By using a formal multi-objective handling scheme based on Pareto dominance criteria, the need to pre-weight competing systemic objective functions is removed and the optimal solution for a design problem can be selected from a front of candidates after the parameter optimization has been completed. MOPAPSO's ability to match MOPSO's results using parallelization for improved performance is presented. Results for both four and ve bar mechanism synthesis examples are shown.","abstract_has_math":false,"creators":["McDougall, Robin David"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Nokleby, Scott"],"committee_chairs":[],"committee_members":[],"year":2008,"date_issued":"2008-12-01","date_published":"2008-12-01","updated_at":"2026-07-24T05:35:16Z","subjects":["multi-objective particle swarm optimization","multi-objective parallel asynchronous particle swarm optimization"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/17","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Nokleby, Scott"]},{"key":"dc:creator","label":"Author","values":["McDougall, Robin David"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2008-12-22T17:06:43Z","2022-03-29T16:33:28Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2008-12-22T17:06:43Z","2022-03-29T16:33:28Z"]},{"key":"dc:date.issued","label":"Date","values":["2008-12-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical 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":["multi-objective particle swarm optimization","multi-objective parallel asynchronous particle swarm 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/10155/17"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["A distributed variant of multi-objective particle swarm optimization (MOPSO) called multi-objective parallel asynchronous particle swarm optimization (MOPAPSO) is presented, and the effects of distribution of objective function calculations to slave processors on the results and performance are investigated and employed for the synthesis of Grashof mechanisms. By using a formal multi-objective handling scheme based on Pareto dominance criteria, the need to pre-weight competing systemic objective functions is removed and the optimal solution for a design problem can be selected from a front of candidates after the parameter optimization has been completed. MOPAPSO's ability to match MOPSO's results using parallelization for improved performance is presented. Results for both four and ve bar mechanism synthesis examples are shown."]},{"key":"dc:title","label":"Title","values":["Optimization-based mechanism synthesis using multi-objective parallel asynchronous particle swarm optimization"]}]}],"canonical_facts":{"dc:contributor.advisor":["Nokleby, Scott"],"dc:creator":["McDougall, Robin David"],"dc:date.accessioned":["2008-12-22T17:06:43Z","2022-03-29T16:33:28Z"],"dc:date.available":["2008-12-22T17:06:43Z","2022-03-29T16:33:28Z"],"dc:date.issued":["2008-12-01"],"dc:description.abstract":["A distributed variant of multi-objective particle swarm optimization (MOPSO) called multi-objective parallel asynchronous particle swarm optimization (MOPAPSO) is presented, and the effects of distribution of objective function calculations to slave processors on the results and performance are investigated and employed for the synthesis of Grashof mechanisms. By using a formal multi-objective handling scheme based on Pareto dominance criteria, the need to pre-weight competing systemic objective functions is removed and the optimal solution for a design problem can be selected from a front of candidates after the parameter optimization has been completed. MOPAPSO's ability to match MOPSO's results using parallelization for improved performance is presented. Results for both four and ve bar mechanism synthesis examples are shown."],"dc:identifier.uri":["https://hdl.handle.net/10155/17"],"dc:language.iso":["en"],"dc:subject":["multi-objective particle swarm optimization","multi-objective parallel asynchronous particle swarm optimization"],"dc:title":["Optimization-based mechanism synthesis using multi-objective parallel asynchronous particle swarm optimization"],"dc:type":["Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:16Z"}