{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1234"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1234","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Proposing effective coordinate search methods for solving large-scale expensive black-box optimization problems","abstract":"In engineering and science, optimization plays a vital role in many real-world applications. In this work, several novel optimization algorithms based on Coordinate Search (CS) algorithm are proposed. CS is a gradient-free technique and we have enhanced them for solving Black-box, non-convex, and expensive large-scale problems. These CS-based algorithms can handle mixed-type variables. When an optimization problem is large-scale and expensive, it is a very challenging problem to solve because it is intersecting two conflicting properties. Large-scale problems require extensive fitness evaluations, but each evaluation is time consuming. It gets more challenging when the budget is limited, which is the case in most real-word applications. The proposed CS-based algorithms reduce the search space exponentially; this makes it a powerful method for optimizing high-dimensional problems with limited budget. The proposed algorithms show a very promising performance on optimizing high-dimensional problems; tested on the CEC-2013 benchmarks problems and neural network training.","abstract_html":"In engineering and science, optimization plays a vital role in many real-world applications. In this work, several novel optimization algorithms based on Coordinate Search (CS) algorithm are proposed. CS is a gradient-free technique and we have enhanced them for solving Black-box, non-convex, and expensive large-scale problems. These CS-based algorithms can handle mixed-type variables. When an optimization problem is large-scale and expensive, it is a very challenging problem to solve because it is intersecting two conflicting properties. Large-scale problems require extensive fitness evaluations, but each evaluation is time consuming. It gets more challenging when the budget is limited, which is the case in most real-word applications. The proposed CS-based algorithms reduce the search space exponentially; this makes it a powerful method for optimizing high-dimensional problems with limited budget. The proposed algorithms show a very promising performance on optimizing high-dimensional problems; tested on the CEC-2013 benchmarks problems and neural network training.","abstract_has_math":false,"creators":["Rokhsatyazdi, Ehsan"],"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":["Rahnamayan, Shahriyar"],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-01","date_published":"2020-08-01","updated_at":"2026-07-24T05:35:34Z","subjects":["Coordinate-search","Gradient-free","Non-convex","Neural-network","Large-scale optimization"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1234","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Rahnamayan, Shahriyar"]},{"key":"dc:creator","label":"Author","values":["Rokhsatyazdi, Ehsan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-02-24T16:23:54Z","2022-03-29T16:49:32Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-02-24T16:23:54Z","2022-03-29T16:49:32Z"]},{"key":"dc:date.issued","label":"Date","values":["2020-08-01"]},{"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":["Coordinate-search","Gradient-free","Non-convex","Neural-network","Large-scale 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/10155/1234"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In engineering and science, optimization plays a vital role in many real-world applications. In this work, several novel optimization algorithms based on Coordinate Search (CS) algorithm are proposed. CS is a gradient-free technique and we have enhanced them for solving Black-box, non-convex, and expensive large-scale problems. These CS-based algorithms can handle mixed-type variables. When an optimization problem is large-scale and expensive, it is a very challenging problem to solve because it is intersecting two conflicting properties. Large-scale problems require extensive fitness evaluations, but each evaluation is time consuming. It gets more challenging when the budget is limited, which is the case in most real-word applications. The proposed CS-based algorithms reduce the search space exponentially; this makes it a powerful method for optimizing high-dimensional problems with limited budget. The proposed algorithms show a very promising performance on optimizing high-dimensional problems; tested on the CEC-2013 benchmarks problems and neural network training."]},{"key":"dc:title","label":"Title","values":["Proposing effective coordinate search methods for solving large-scale expensive black-box optimization problems"]}]}],"canonical_facts":{"dc:contributor.advisor":["Rahnamayan, Shahriyar"],"dc:creator":["Rokhsatyazdi, Ehsan"],"dc:date.accessioned":["2021-02-24T16:23:54Z","2022-03-29T16:49:32Z"],"dc:date.available":["2021-02-24T16:23:54Z","2022-03-29T16:49:32Z"],"dc:date.issued":["2020-08-01"],"dc:description.abstract":["In engineering and science, optimization plays a vital role in many real-world applications. In this work, several novel optimization algorithms based on Coordinate Search (CS) algorithm are proposed. CS is a gradient-free technique and we have enhanced them for solving Black-box, non-convex, and expensive large-scale problems. These CS-based algorithms can handle mixed-type variables. When an optimization problem is large-scale and expensive, it is a very challenging problem to solve because it is intersecting two conflicting properties. Large-scale problems require extensive fitness evaluations, but each evaluation is time consuming. It gets more challenging when the budget is limited, which is the case in most real-word applications. The proposed CS-based algorithms reduce the search space exponentially; this makes it a powerful method for optimizing high-dimensional problems with limited budget. The proposed algorithms show a very promising performance on optimizing high-dimensional problems; tested on the CEC-2013 benchmarks problems and neural network training."],"dc:identifier.uri":["https://hdl.handle.net/10155/1234"],"dc:language.iso":["en"],"dc:subject":["Coordinate-search","Gradient-free","Non-convex","Neural-network","Large-scale optimization"],"dc:title":["Proposing effective coordinate search methods for solving large-scale expensive black-box optimization problems"],"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":"2026-07-24T05:35:34Z"}