{"id":"https://openalex.org/W4401414454","doi":"https://doi.org/10.1109/cec60901.2024.10611854","title":"A Meta-Learning - Based Surrogate-Assisted Evolutionary Algorithm for Expensive Multi-Objective Optimization Problems","display_name":"A Meta-Learning - Based Surrogate-Assisted Evolutionary Algorithm for Expensive Multi-Objective Optimization Problems","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4401414454","doi":"https://doi.org/10.1109/cec60901.2024.10611854"},"language":"en","primary_location":{"id":"doi:10.1109/cec60901.2024.10611854","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec60901.2024.10611854","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 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/A5065316341","display_name":"Liqun Wen","orcid":null},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liqun Wen","raw_affiliation_strings":["College of Information Science and Engineering Northeastern University,Shenyang,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering Northeastern University,Shenyang,China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100674676","display_name":"Hongfeng Wang","orcid":"https://orcid.org/0000-0002-8954-0876"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongfeng Wang","raw_affiliation_strings":["College of Information Science and Engineering Northeastern University,Shenyang,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering Northeastern University,Shenyang,China","institution_ids":["https://openalex.org/I9224756"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I9224756"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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":0.993399977684021,"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/T10998","display_name":"Heat Transfer and Optimization","score":0.9901999831199646,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.679989218711853},{"id":"https://openalex.org/keywords/evolutionary-algorithm","display_name":"Evolutionary algorithm","score":0.636055588722229},{"id":"https://openalex.org/keywords/surrogate-model","display_name":"Surrogate model","score":0.4964023232460022},{"id":"https://openalex.org/keywords/evolutionary-computation","display_name":"Evolutionary computation","score":0.4855479896068573},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.48418882489204407},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46421197056770325},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3646394610404968},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1925187110900879}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.679989218711853},{"id":"https://openalex.org/C159149176","wikidata":"https://www.wikidata.org/wiki/Q14489129","display_name":"Evolutionary algorithm","level":2,"score":0.636055588722229},{"id":"https://openalex.org/C131675550","wikidata":"https://www.wikidata.org/wiki/Q7646884","display_name":"Surrogate model","level":2,"score":0.4964023232460022},{"id":"https://openalex.org/C105902424","wikidata":"https://www.wikidata.org/wiki/Q1197129","display_name":"Evolutionary computation","level":2,"score":0.4855479896068573},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.48418882489204407},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46421197056770325},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3646394610404968},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1925187110900879}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cec60901.2024.10611854","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cec60901.2024.10611854","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 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":31,"referenced_works":["https://openalex.org/W1553573873","https://openalex.org/W2011174137","https://openalex.org/W2012451526","https://openalex.org/W2098907614","https://openalex.org/W2105245738","https://openalex.org/W2111526171","https://openalex.org/W2143381319","https://openalex.org/W2150046657","https://openalex.org/W2152551290","https://openalex.org/W2155035377","https://openalex.org/W2166739626","https://openalex.org/W2546299924","https://openalex.org/W2560674852","https://openalex.org/W2764251381","https://openalex.org/W2785722638","https://openalex.org/W2785988364","https://openalex.org/W2891186800","https://openalex.org/W2917041815","https://openalex.org/W2919115771","https://openalex.org/W2951104886","https://openalex.org/W3004157659","https://openalex.org/W3034942609","https://openalex.org/W3163842339","https://openalex.org/W4294646197","https://openalex.org/W4300971732","https://openalex.org/W4377091737","https://openalex.org/W6729433768","https://openalex.org/W6736057607","https://openalex.org/W6750254146","https://openalex.org/W6752515464","https://openalex.org/W6760210782"],"related_works":["https://openalex.org/W4297582752","https://openalex.org/W4285805405","https://openalex.org/W2391924736","https://openalex.org/W3133779647","https://openalex.org/W2887328214","https://openalex.org/W3192856315","https://openalex.org/W4293363729","https://openalex.org/W1560122427","https://openalex.org/W2021957875","https://openalex.org/W2802808995"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"expensive":[3,36,53,89,120,170],"multi-objective":[4,90],"optimization":[5,37,91,154],"problems":[6],"that":[7,160],"involve":[8],"the":[9,21,43,46,58,72,75,112,116,130,137,140,143,149,152,166,173],"costly-computation":[10],"of":[11,23,45,52,74,115,139,169,180],"multiple":[12],"competing":[13],"objectives":[14],"have":[15],"gained":[16],"an":[17],"increasing":[18],"concern":[19],"from":[20,105],"community":[22],"evolutionary":[24,27,153],"computation.":[25],"Surrogate-assisted":[26],"algorithms":[28],"(SAEA)":[29],"are":[30,109,123],"often":[31],"utilized":[32,110,124,147],"to":[33,57,70,101,128,135],"address":[34],"such":[35],"problems.":[38,92,183],"However,":[39],"in":[40,148],"many":[41],"SAEAs,":[42],"initialization":[44,73,138],"surrogate":[47,76,145],"model":[48,77],"usually":[49],"requires":[50],"lots":[51],"function":[54,63,121],"evaluations.":[55],"Due":[56],"limited":[59],"budget":[60,168],"for":[61,87,125,151],"real":[62],"evaluations,":[64],"it":[65],"is":[66,146,175],"a":[67,84,97,178],"great":[68],"challenge":[69],"complete":[71,136],"using":[78],"few-shot":[79],"data.":[80],"This":[81,93],"paper":[82],"proposes":[83],"meta-learning-based":[85],"SAEA":[86],"solving":[88],"algorithm":[94,100,162],"first":[95],"uses":[96],"gradient-based":[98],"meta-learning":[99],"learn":[102],"domain-specific":[103],"features":[104],"related":[106],"tasks,":[107],"which":[108],"as":[111],"common":[113],"parameters":[114,133],"surrogate;":[117,141],"then,":[118],"few":[119],"evaluations":[122],"fast":[126],"adaptation":[127],"obtain":[129],"target":[131],"task-specific":[132],"and":[134,172],"finally,":[142],"initialized":[144],"MOEA/D-DE":[150],"process.":[155],"Numerical":[156],"experimental":[157],"results":[158],"demonstrate":[159],"this":[161],"could":[163],"effectively":[164],"save":[165],"evaluation":[167],"functions,":[171],"effectiveness":[174],"verified":[176],"on":[177],"set":[179],"benchmark":[181],"test":[182]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
