{"id":{"repo_id":"laurentian","oai_identifier":"oai:laurentian.scholaris.ca:10219/3552"},"canonical_url":"https://search.dev.ndltd.org/etd/laurentian/oai:laurentian.scholaris.ca:10219/3552","repository":{"repo_id":"laurentian","name":"Laurentian University","base_url":"https://laurentian.scholaris.ca/server/oai/request"},"display":{"title":"Global 0ptimization of pairwise comparisons matrix based on differential evolution algorithm","abstract":"Pairwise comparisons matrices (PCM) are commonly used when different entities or abstract concepts are compared for making decisions. The compared entities for decision making can be both subjective and objective indicators. The elements in PCM are ratios in case of multiplicative version. Using differential evolution (DE) heuristic algorithm, we can find an optimal solution for a given PCM. The optimization results are fairly good with geometric mean (GM) and eigenvector (EV). The thesis provides an introduction of differential evolution and how to apply it to the global optimization of PC matrices. IDEs and Java/R packages used in the Monte Carlo experiment will be discussed. Some results of considerable importance for PC matrices that have been obtained will also be illustrated in the thesis.","abstract_html":"Pairwise comparisons matrices (PCM) are commonly used when different entities or abstract concepts are compared for making decisions. The compared entities for decision making can be both subjective and objective indicators. The elements in PCM are ratios in case of multiplicative version. Using differential evolution (DE) heuristic algorithm, we can find an optimal solution for a given PCM. The optimization results are fairly good with geometric mean (GM) and eigenvector (EV). The thesis provides an introduction of differential evolution and how to apply it to the global optimization of PC matrices. IDEs and Java/R packages used in the Monte Carlo experiment will be discussed. Some results of considerable importance for PC matrices that have been obtained will also be illustrated in the thesis.","abstract_has_math":false,"creators":["Duan, Yuqing"],"institution":"Laurentian University of Sudbury","degree_name":"Master of Science (MSc) in Computational Sciences","degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-07-22","date_published":"2019-07-22","updated_at":"2026-08-21T16:45:57Z","subjects":["Pairwise comparisons matrices","differential evolution heuristic","optmization","geometric mean","eigenvector","Monte Carlo experiment","Java, R."],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://laurentian.scholaris.ca/handle/10219/3552","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"source_record":{"url":"https://laurentian.scholaris.ca/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Alaurentian.scholaris.ca%3A10219%2F3552","prefix":"dim"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Duan, Yuqing"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2020-07-29T17:46:07Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2020-07-29T17:46:07Z"]},{"key":"dc:date.issued","label":"Date","values":["2019-07-22"]},{"key":"dc:publisher","label":"Institution","values":["Laurentian University of Sudbury"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc) in Computational Sciences"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Laurentian University of Sudbury"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Pairwise comparisons matrices","differential evolution heuristic","optmization","geometric mean","eigenvector","Monte Carlo experiment","Java, R."]}]},{"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://laurentian.scholaris.ca/handle/10219/3552"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Pairwise comparisons matrices (PCM) are commonly used when different entities or abstract concepts are compared for making decisions. The compared entities for decision making can be both subjective and objective indicators. The elements in PCM are ratios in case of multiplicative version. Using differential evolution (DE) heuristic algorithm, we can find an optimal solution for a given PCM. The optimization results are fairly good with geometric mean (GM) and eigenvector (EV). The thesis provides an introduction of differential evolution and how to apply it to the global optimization of PC matrices. IDEs and Java/R packages used in the Monte Carlo experiment will be discussed. Some results of considerable importance for PC matrices that have been obtained will also be illustrated in the thesis."]},{"key":"dc:title","label":"Title","values":["Global 0ptimization of pairwise comparisons matrix based on differential evolution algorithm"]}]}],"canonical_facts":{"dc:creator":["Duan, Yuqing"],"dc:date.accessioned":["2020-07-29T17:46:07Z"],"dc:date.available":["2020-07-29T17:46:07Z"],"dc:date.issued":["2019-07-22"],"dc:description.abstract":["Pairwise comparisons matrices (PCM) are commonly used when different entities or abstract concepts are compared for making decisions. The compared entities for decision making can be both subjective and objective indicators. The elements in PCM are ratios in case of multiplicative version. Using differential evolution (DE) heuristic algorithm, we can find an optimal solution for a given PCM. The optimization results are fairly good with geometric mean (GM) and eigenvector (EV). The thesis provides an introduction of differential evolution and how to apply it to the global optimization of PC matrices. IDEs and Java/R packages used in the Monte Carlo experiment will be discussed. Some results of considerable importance for PC matrices that have been obtained will also be illustrated in the thesis."],"dc:identifier.uri":["https://laurentian.scholaris.ca/handle/10219/3552"],"dc:language.iso":["en"],"dc:publisher":["Laurentian University of Sudbury"],"dc:subject":["Pairwise comparisons matrices","differential evolution heuristic","optmization","geometric mean","eigenvector","Monte Carlo experiment","Java, R."],"dc:title":["Global 0ptimization of pairwise comparisons matrix based on differential evolution algorithm"],"dc:type":["Thesis"],"thesis:degree_name":["Master of Science (MSc) in Computational Sciences"],"thesis:institution_name":["Laurentian University of Sudbury"]},"updated_at":"2026-08-21T16:45:57Z"}