{"id":"https://openalex.org/W3157100883","doi":"https://doi.org/10.1109/tnnls.2022.3148435","title":"Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problems","display_name":"Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problems","publication_year":2022,"publication_date":"2022-02-16","ids":{"openalex":"https://openalex.org/W3157100883","doi":"https://doi.org/10.1109/tnnls.2022.3148435","mag":"3157100883","pmid":"https://pubmed.ncbi.nlm.nih.gov/35171781"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2022.3148435","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3148435","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zizhen Zhang","orcid":"https://orcid.org/0000-0003-0320-9355"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zizhen Zhang","raw_affiliation_strings":["Guangdong Key Laboratory of Big Data Analysis and Processing and the School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangdong Key Laboratory of Big Data Analysis and Processing and the School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhiyuan Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyuan Wu","raw_affiliation_strings":["Guangdong Key Laboratory of Big Data Analysis and Processing and the School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangdong Key Laboratory of Big Data Analysis and Processing and the School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hang Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hang Zhang","raw_affiliation_strings":["Guangdong Key Laboratory of Big Data Analysis and Processing and the School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangdong Key Laboratory of Big Data Analysis and Processing and the School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":null,"display_name":"Jiahai Wang","orcid":"https://orcid.org/0000-0002-6961-7813"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiahai Wang","raw_affiliation_strings":["Guangdong Key Laboratory of Big Data Analysis and Processing and the School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangdong Key Laboratory of Big Data Analysis and Processing and the School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":15.2385,"has_fulltext":false,"cited_by_count":111,"citation_normalized_percentile":{"value":0.99330885,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"34","issue":"10","first_page":"7978","last_page":"7991"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.8708999752998352,"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":0.8708999752998352,"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/T10567","display_name":"Vehicle Routing Optimization Methods","score":0.03590000048279762,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.009499999694526196,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.6061999797821045},{"id":"https://openalex.org/keywords/multi-objective-optimization","display_name":"Multi-objective optimization","score":0.6050999760627747},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.5322999954223633},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.5047000050544739},{"id":"https://openalex.org/keywords/travelling-salesman-problem","display_name":"Travelling salesman problem","score":0.447299987077713},{"id":"https://openalex.org/keywords/train","display_name":"Train","score":0.4401000142097473},{"id":"https://openalex.org/keywords/pareto-principle","display_name":"Pareto principle","score":0.4275999963283539},{"id":"https://openalex.org/keywords/pareto-optimal","display_name":"Pareto optimal","score":0.3968000113964081}],"concepts":[{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.7245000004768372},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6061999797821045},{"id":"https://openalex.org/C68781425","wikidata":"https://www.wikidata.org/wiki/Q2052203","display_name":"Multi-objective optimization","level":2,"score":0.6050999760627747},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5834000110626221},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.5322999954223633},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.5047000050544739},{"id":"https://openalex.org/C175859090","wikidata":"https://www.wikidata.org/wiki/Q322212","display_name":"Travelling salesman problem","level":2,"score":0.447299987077713},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.4401000142097473},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.4275999963283539},{"id":"https://openalex.org/C2986314615","wikidata":"https://www.wikidata.org/wiki/Q36829","display_name":"Pareto optimal","level":3,"score":0.3968000113964081},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3817000091075897},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.34279999136924744},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3393000066280365},{"id":"https://openalex.org/C52692508","wikidata":"https://www.wikidata.org/wiki/Q1333872","display_name":"Combinatorial optimization","level":2,"score":0.3050000071525574},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.298799991607666},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2856000065803528},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.27149999141693115},{"id":"https://openalex.org/C123784306","wikidata":"https://www.wikidata.org/wiki/Q944041","display_name":"Vehicle routing problem","level":3,"score":0.2619999945163727},{"id":"https://openalex.org/C62469222","wikidata":"https://www.wikidata.org/wiki/Q17092103","display_name":"Hybrid algorithm (constraint satisfaction)","level":5,"score":0.257099986076355},{"id":"https://openalex.org/C106472803","wikidata":"https://www.wikidata.org/wiki/Q291440","display_name":"2-opt","level":3,"score":0.25360000133514404}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2022.3148435","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3148435","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:35171781","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35171781","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G313307663","display_name":null,"funder_award_id":"2019A1515011169","funder_id":"https://openalex.org/F4320321921","funder_display_name":"Natural Science Foundation of Guangdong Province"},{"id":"https://openalex.org/G4233013071","display_name":null,"funder_award_id":"2018AAA0101203","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G4432671367","display_name":null,"funder_award_id":"71601191","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G710202329","display_name":null,"funder_award_id":"62072483","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7329657632","display_name":null,"funder_award_id":"2021A1515011301","funder_id":"https://openalex.org/F4320321921","funder_display_name":"Natural Science Foundation of Guangdong Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321921","display_name":"Natural Science Foundation of Guangdong Province","ror":null},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W142294586","https://openalex.org/W178407265","https://openalex.org/W1803713235","https://openalex.org/W1987725948","https://openalex.org/W2008499862","https://openalex.org/W2012612381","https://openalex.org/W2036643871","https://openalex.org/W2038507294","https://openalex.org/W2096305430","https://openalex.org/W2126105956","https://openalex.org/W2143381319","https://openalex.org/W2163428398","https://openalex.org/W2194775991","https://openalex.org/W2616650140","https://openalex.org/W2764251381","https://openalex.org/W2805798351","https://openalex.org/W2940676189","https://openalex.org/W2948175577","https://openalex.org/W3004325772","https://openalex.org/W3009895648","https://openalex.org/W3014847873","https://openalex.org/W3027146252","https://openalex.org/W3043239066","https://openalex.org/W3047863327","https://openalex.org/W3112776497","https://openalex.org/W3113123584","https://openalex.org/W3117777470","https://openalex.org/W3133471011","https://openalex.org/W3175494061","https://openalex.org/W3177337956","https://openalex.org/W3199469541","https://openalex.org/W4211208325","https://openalex.org/W6631190155","https://openalex.org/W6638667902","https://openalex.org/W6725207838","https://openalex.org/W6730742100","https://openalex.org/W6736057607","https://openalex.org/W6736495275","https://openalex.org/W6739901393","https://openalex.org/W6748487558","https://openalex.org/W6750254146","https://openalex.org/W6758687306","https://openalex.org/W6762840543","https://openalex.org/W6770961093","https://openalex.org/W6771561114","https://openalex.org/W6774524600","https://openalex.org/W6784473743"],"related_works":[],"abstract_inverted_index":{"Deep":[0],"reinforcement":[1],"learning":[2],"(DRL)":[3],"has":[4],"recently":[5],"shown":[6],"its":[7],"success":[8],"in":[9],"tackling":[10],"complex":[11],"combinatorial":[12],"optimization":[13],"problems.":[14],"When":[15],"these":[16],"problems":[17,134,139],"are":[18],"extended":[19],"to":[20,31,70,101,117],"multiobjective":[21,131,136],"ones,":[22],"it":[23],"becomes":[24],"difficult":[25],"for":[26,73],"the":[27,41,74,94,102,108,119,124,144,152],"existing":[28],"DRL":[29,52],"approaches":[30],"flexibly":[32],"and":[33,104,121,135,154],"efficiently":[34],"deal":[35],"with":[36,65,85,140],"multiple":[37,98],"subproblems":[38],"determined":[39],"by":[40,59],"weight":[42],"decomposition":[43],"of":[44,97,107,123,146,151],"objectives.":[45],"This":[46],"article":[47],"proposes":[48],"a":[49,57,66],"concise":[50],"meta-learning-based":[51],"approach.":[53],"It":[54],"first":[55],"trains":[56],"meta-model":[58,62],"meta-learning.":[60],"The":[61,77,127],"is":[63,80],"fine-tuned":[64],"few":[67],"update":[68],"steps":[69],"derive":[71],"submodels":[72,111],"corresponding":[75],"subproblems.":[76],"Pareto":[78],"front":[79],"then":[81],"built":[82],"accordingly.":[83],"Compared":[84],"other":[86],"learning-based":[87,153],"methods,":[88],"our":[89,147],"method":[90,148],"can":[91,112],"greatly":[92],"shorten":[93],"training":[95],"time":[96,141],"submodels.":[99],"Due":[100],"rapid":[103],"excellent":[105],"adaptability":[106],"meta-model,":[109],"more":[110],"be":[113],"derived":[114],"so":[115],"as":[116],"increase":[118],"quality":[120],"diversity":[122],"found":[125],"solutions.":[126],"computational":[128],"experiments":[129],"on":[130],"traveling":[132],"salesman":[133],"vehicle":[137],"routing":[138],"windows":[142],"demonstrate":[143],"superiority":[145],"over":[149],"most":[150],"iteration-based":[155],"approaches.":[156]},"counts_by_year":[{"year":2026,"cited_by_count":10},{"year":2025,"cited_by_count":43},{"year":2024,"cited_by_count":36},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":8}],"updated_date":"2026-04-14T08:04:32.555800","created_date":"2021-05-10T00:00:00"}