{"id":"https://openalex.org/W4382372076","doi":"https://doi.org/10.1109/tevc.2023.3290172","title":"Multiobjective Ensemble Learning With Multiscale Data for Product Quality Prediction in Iron and Steel Industry","display_name":"Multiobjective Ensemble Learning With Multiscale Data for Product Quality Prediction in Iron and Steel Industry","publication_year":2023,"publication_date":"2023-06-28","ids":{"openalex":"https://openalex.org/W4382372076","doi":"https://doi.org/10.1109/tevc.2023.3290172"},"language":"en","primary_location":{"id":"doi:10.1109/tevc.2023.3290172","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tevc.2023.3290172","pdf_url":null,"source":{"id":"https://openalex.org/S93787993","display_name":"IEEE Transactions on Evolutionary Computation","issn_l":"1089-778X","issn":["1089-778X","1941-0026"],"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 Evolutionary Computation","raw_type":"journal-article"},"type":"article","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/A5080165465","display_name":"Xianpeng Wang","orcid":"https://orcid.org/0000-0001-8132-9446"},"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":"Xianpeng Wang","raw_affiliation_strings":["National Frontiers Science Center for Industrial Intelligence and Systems Optimization, the Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0001-8132-9446","affiliations":[{"raw_affiliation_string":"National Frontiers Science Center for Industrial Intelligence and Systems Optimization, the Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017293692","display_name":"Yao Wang","orcid":"https://orcid.org/0000-0002-1014-2788"},"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":"Yao Wang","raw_affiliation_strings":["Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry, the Liaoning Key Laboratory of Manufacturing System and Logistics Optimization, Northeastern University, Shenyang, China","the Liaoning Key Laboratory of Manufacturing System and Logistics Optimization, Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Shenyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry, the Liaoning Key Laboratory of Manufacturing System and Logistics Optimization, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]},{"raw_affiliation_string":"the Liaoning Key Laboratory of Manufacturing System and Logistics Optimization, Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080304378","display_name":"Lixin Tang","orcid":"https://orcid.org/0000-0002-9950-5169"},"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":"Lixin Tang","raw_affiliation_strings":["National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0002-9950-5169","affiliations":[{"raw_affiliation_string":"National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000546219","display_name":"Qingfu Zhang","orcid":"https://orcid.org/0000-0003-0786-0671"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Qingfu Zhang","raw_affiliation_strings":["Department of Computer Science, City University of Hong Kong, Hong Kong, China"],"raw_orcid":"https://orcid.org/0000-0003-0786-0671","affiliations":[{"raw_affiliation_string":"Department of Computer Science, City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.1737,"has_fulltext":false,"cited_by_count":37,"citation_normalized_percentile":{"value":0.9521783,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"28","issue":"4","first_page":"1099","last_page":"1113"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12282","display_name":"Mineral Processing and Grinding","score":0.9757000207901001,"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"}},"topics":[{"id":"https://openalex.org/T12282","display_name":"Mineral Processing and Grinding","score":0.9757000207901001,"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"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.9728999733924866,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11837","display_name":"Iron and Steelmaking Processes","score":0.9660999774932861,"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":0.7123807072639465},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5944094061851501},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5656279921531677},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5651960968971252},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5265964269638062},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5192201733589172},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.505896270275116},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.49362409114837646},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45068636536598206},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.4247472882270813},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4245944321155548},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3648588955402374}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7123807072639465},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5944094061851501},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5656279921531677},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5651960968971252},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5265964269638062},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5192201733589172},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.505896270275116},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.49362409114837646},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45068636536598206},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.4247472882270813},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4245944321155548},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3648588955402374},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tevc.2023.3290172","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tevc.2023.3290172","pdf_url":null,"source":{"id":"https://openalex.org/S93787993","display_name":"IEEE Transactions on Evolutionary Computation","issn_l":"1089-778X","issn":["1089-778X","1941-0026"],"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 Evolutionary Computation","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.550000011920929}],"awards":[{"id":"https://openalex.org/G2311391187","display_name":null,"funder_award_id":"B16009","funder_id":"https://openalex.org/F4320327912","funder_display_name":"Higher Education Discipline Innovation Project"},{"id":"https://openalex.org/G5303336969","display_name":null,"funder_award_id":"72192830","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5732221011","display_name":null,"funder_award_id":"62073067","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G852931893","display_name":null,"funder_award_id":"72192831","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320327912","display_name":"Higher Education Discipline Innovation Project","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W225560312","https://openalex.org/W1522538275","https://openalex.org/W1525375343","https://openalex.org/W1677182931","https://openalex.org/W2039708501","https://openalex.org/W2074616700","https://openalex.org/W2126105956","https://openalex.org/W2133176659","https://openalex.org/W2143381319","https://openalex.org/W2471161958","https://openalex.org/W2593649365","https://openalex.org/W2736299552","https://openalex.org/W2791865700","https://openalex.org/W2794291692","https://openalex.org/W2796628781","https://openalex.org/W2887063112","https://openalex.org/W2887065738","https://openalex.org/W2911964244","https://openalex.org/W2912562071","https://openalex.org/W2940678998","https://openalex.org/W2942322192","https://openalex.org/W2946547492","https://openalex.org/W2953308748","https://openalex.org/W2963784900","https://openalex.org/W2963946985","https://openalex.org/W2972260659","https://openalex.org/W2976026359","https://openalex.org/W3033716081","https://openalex.org/W3034190797","https://openalex.org/W3080409317","https://openalex.org/W3088798822","https://openalex.org/W3091563199","https://openalex.org/W3100777112","https://openalex.org/W3109552991","https://openalex.org/W3134254543","https://openalex.org/W3165140781","https://openalex.org/W3167548703","https://openalex.org/W3171771940","https://openalex.org/W3187004996","https://openalex.org/W3188304288","https://openalex.org/W3192682950","https://openalex.org/W3201276444","https://openalex.org/W4205678345","https://openalex.org/W4212883601","https://openalex.org/W4214660441","https://openalex.org/W4226502504","https://openalex.org/W4312222038","https://openalex.org/W6608886761","https://openalex.org/W6631190155","https://openalex.org/W6631460888","https://openalex.org/W6680850120","https://openalex.org/W6766978945"],"related_works":["https://openalex.org/W2349012012","https://openalex.org/W2794896638","https://openalex.org/W2891633941","https://openalex.org/W3202800081","https://openalex.org/W3101614107","https://openalex.org/W1909207154","https://openalex.org/W4390971112","https://openalex.org/W3036530763","https://openalex.org/W3124390867","https://openalex.org/W1514365828"],"abstract_inverted_index":{"High":[0],"quality":[1,3,165,177,204],"product":[2,44,176,203],"prediction":[4,20,126,166,205],"is":[5,75,120,190],"very":[6],"important":[7],"for":[8],"iron":[9,184],"and":[10,53,101,110,138,152,160,185,210],"steel":[11,186],"enterprises":[12],"to":[13,106,122,192,200],"ensure":[14],"stable":[15],"production.":[16],"However,":[17],"most":[18],"existing":[19,163],"methods":[21,28],"are":[22,48],"manually":[23],"designed":[24],"learning":[25,68,108,158],"models.":[26,167],"These":[27],"consider":[29],"only":[30],"macroscopic":[31],"data":[32,36,72,78,82,137,140,199],"while":[33],"ignoring":[34],"mesoscopic":[35],"that":[37,145],"also":[38],"have":[39],"a":[40,62,90,112,202],"significant":[41],"impact":[42],"on":[43,117,134],"quality.":[45],"Thus,":[46],"they":[47],"often":[49],"poor":[50],"at":[51],"accuracy":[52,151,209],"generalization":[54],"performance":[55],"in":[56,174,182],"practice.":[57],"To":[58],"address":[59],"this":[60],"issue,":[61],"multi-objective":[63],"convolutional":[64,93],"neural":[65,94],"networks":[66,95],"ensemble":[67,114],"method":[69,170],"with":[70,155,196,207],"multi-scale":[71],"fusion":[73,79],"(MOCNNEL-MSDF)":[74],"developed.":[76],"Using":[77],"of":[80,92,141,179],"macro/meso":[81],"derived":[83],"from":[84,128],"kinetic":[85],"models,":[86],"MOCNNEL-MSDF":[87,146],"first":[88],"evolves":[89],"swarm":[91],"(CNNs)":[96],"by":[97],"knowledge-transferring":[98],"based":[99,116],"reproduction":[100],"adaptive":[102],"weights":[103],"initialization":[104],"adjustment":[105],"improve":[107],"performance,":[109],"then":[111],"sparse":[113],"approach":[115],"differential":[118],"evolution":[119],"applied":[121],"achieve":[123],"the":[124,129,162,175,183],"final":[125],"model":[127,206],"evolved":[130],"CNNs.":[131],"Experimental":[132],"results":[133],"both":[135],"benchmark":[136],"practical":[139],"continuous":[142],"annealing":[143],"show":[144],"achieves":[147],"competitive":[148],"or":[149],"better":[150],"robustness":[153],"compared":[154],"other":[156],"powerful":[157],"methods,":[159],"outperforms":[161],"strip":[164],"The":[168],"proposed":[169],"can":[171],"be":[172],"used":[173],"modeling":[178],"each":[180],"process":[181,198],"industry,":[187],"where":[188],"it":[189],"desirable":[191],"combine":[193],"mechanism":[194],"models":[195],"production":[197],"construct":[201],"higher":[208],"generalization.":[211]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":19},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
