{"id":"https://openalex.org/W3083125375","doi":"https://doi.org/10.1109/cec48606.2020.9185549","title":"MOEA/D-S<sup>3</sup>: MOEA/D using SVM-based Surrogates adjusted to Subproblems for Many objective optimization","display_name":"MOEA/D-S<sup>3</sup>: MOEA/D using SVM-based Surrogates adjusted to Subproblems for Many objective optimization","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3083125375","doi":"https://doi.org/10.1109/cec48606.2020.9185549","mag":"3083125375"},"language":"en","primary_location":{"id":"doi:10.1109/cec48606.2020.9185549","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec48606.2020.9185549","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Congress on Evolutionary Computation (CEC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088012252","display_name":"Takumi Sonoda","orcid":null},"institutions":[{"id":"https://openalex.org/I180203408","display_name":"Yokohama National University","ror":"https://ror.org/03zyp6p76","country_code":"JP","type":"education","lineage":["https://openalex.org/I180203408"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takumi Sonoda","raw_affiliation_strings":["Collage of Engineering Science, Yokohama National University, Yokohama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Collage of Engineering Science, Yokohama National University, Yokohama, Japan","institution_ids":["https://openalex.org/I180203408"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019516292","display_name":"Masaya Nakata","orcid":"https://orcid.org/0000-0003-3428-7890"},"institutions":[{"id":"https://openalex.org/I180203408","display_name":"Yokohama National University","ror":"https://ror.org/03zyp6p76","country_code":"JP","type":"education","lineage":["https://openalex.org/I180203408"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masaya Nakata","raw_affiliation_strings":["Collage of Engineering Science, Yokohama National University, Yokohama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Collage of Engineering Science, Yokohama National University, Yokohama, Japan","institution_ids":["https://openalex.org/I180203408"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I180203408"],"apc_list":null,"apc_paid":null,"fwci":2.3586,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.88905579,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9865000247955322,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.754635214805603},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.6668509244918823},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.6421916484832764},{"id":"https://openalex.org/keywords/surrogate-model","display_name":"Surrogate model","score":0.5724804997444153},{"id":"https://openalex.org/keywords/pareto-principle","display_name":"Pareto principle","score":0.5571269989013672},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5564777255058289},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5540487170219421},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5413508415222168},{"id":"https://openalex.org/keywords/evolutionary-algorithm","display_name":"Evolutionary algorithm","score":0.5262671709060669},{"id":"https://openalex.org/keywords/weight","display_name":"Weight","score":0.5019457340240479},{"id":"https://openalex.org/keywords/multi-objective-optimization","display_name":"Multi-objective optimization","score":0.43335914611816406},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4186887741088867},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38682225346565247},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2977622151374817}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.754635214805603},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.6668509244918823},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.6421916484832764},{"id":"https://openalex.org/C131675550","wikidata":"https://www.wikidata.org/wiki/Q7646884","display_name":"Surrogate model","level":2,"score":0.5724804997444153},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.5571269989013672},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5564777255058289},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5540487170219421},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5413508415222168},{"id":"https://openalex.org/C159149176","wikidata":"https://www.wikidata.org/wiki/Q14489129","display_name":"Evolutionary algorithm","level":2,"score":0.5262671709060669},{"id":"https://openalex.org/C183877218","wikidata":"https://www.wikidata.org/wiki/Q7979858","display_name":"Weight","level":3,"score":0.5019457340240479},{"id":"https://openalex.org/C68781425","wikidata":"https://www.wikidata.org/wiki/Q2052203","display_name":"Multi-objective optimization","level":2,"score":0.43335914611816406},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4186887741088867},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38682225346565247},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2977622151374817},{"id":"https://openalex.org/C51568863","wikidata":"https://www.wikidata.org/wiki/Q664495","display_name":"Lie algebra","level":2,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cec48606.2020.9185549","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec48606.2020.9185549","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Congress on Evolutionary Computation (CEC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1558919105","https://openalex.org/W1608317386","https://openalex.org/W1657213141","https://openalex.org/W1971066704","https://openalex.org/W1999480972","https://openalex.org/W2011174137","https://openalex.org/W2012451526","https://openalex.org/W2067923752","https://openalex.org/W2087347434","https://openalex.org/W2105245738","https://openalex.org/W2109042184","https://openalex.org/W2111526171","https://openalex.org/W2126105956","https://openalex.org/W2143381319","https://openalex.org/W2150046657","https://openalex.org/W2187089797","https://openalex.org/W2419109832","https://openalex.org/W2546299924","https://openalex.org/W2785722638","https://openalex.org/W2911994132","https://openalex.org/W6633513437","https://openalex.org/W6676279030"],"related_works":["https://openalex.org/W2090178682","https://openalex.org/W2001591765","https://openalex.org/W4241467429","https://openalex.org/W2906115061","https://openalex.org/W2977596624","https://openalex.org/W2073147994","https://openalex.org/W1588199609","https://openalex.org/W2145877535","https://openalex.org/W2744730182","https://openalex.org/W2384474142"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,26,36,43,58,110],"surrogate-assisted":[4],"MOEA/D":[5,89,108],"using":[6],"SVM-based":[7],"surrogates":[8,72],"adjusted":[9,82],"to":[10,15,24,33,83,93],"subproblems":[11],"(MOEA/DS3),":[12],"which":[13],"intends":[14],"achieve":[16],"the":[17,68,88,99],"following":[18],"technical":[19],"advantages.":[20],"Firstly,":[21],"in":[22,87,91],"order":[23,92],"construct":[25,34],"proper":[27],"surrogate":[28,37],"while":[29],"reducing":[30],"learning":[31,52],"cost":[32,53],"surrogates,":[35],"is":[38,81],"an":[39,79],"SVMclassifier":[40],"that":[41,105],"identifies":[42],"specific":[44],"region":[45],"of":[46,98,112],"good":[47],"solutions":[48],"and":[49,75,96],"thus":[50],"its":[51],"should":[54],"be":[55],"lower":[56],"than":[57],"popular":[59],"alternative":[60],"approach,":[61],"i.e.,":[62],"fitness":[63],"approximation.":[64],"Secondly,":[65],"relying":[66],"on":[67,109],"first":[69],"advantage,":[70],"multiple":[71],"are":[73],"constructed":[74],"each":[76,84],"surrogate,":[77],"like":[78],"expert,":[80],"subproblem":[85],"defined":[86],"framework":[90],"improve":[94],"diversity":[95],"convergence":[97],"Pareto":[100],"set.":[101],"Experimental":[102],"results":[103],"show":[104],"MOEA/D-S3":[106],"outperforms":[107],"number":[111],"manyobjective":[113],"benchmark":[114],"problems.":[115]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3}],"updated_date":"2026-08-12T21:12:35.861297","created_date":"2025-10-10T00:00:00"}