Conditional Generative Adversarial Network-Based Bilevel Evolutionary Multiobjective Optimization Algorithm
In bilevel multiobjective optimization problems (BLMOPs), the mapping from an upper-level vector to the corresponding lower-level optimal vectors is a complex set valued mapping. Existing methods require numerous surrogate models to fit such a set valued mapping by grouping the lower-level optimal v...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on evolutionary computation Jg. 28; H. 5; S. 1205 - 1219 |
|---|---|
| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
01.10.2024
|
| Schlagworte: | |
| ISSN: | 1089-778X, 1941-0026 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | In bilevel multiobjective optimization problems (BLMOPs), the mapping from an upper-level vector to the corresponding lower-level optimal vectors is a complex set valued mapping. Existing methods require numerous surrogate models to fit such a set valued mapping by grouping the lower-level optimal vectors, and the effects are not satisfactory because the correlation among lower-level optimal vectors corresponding to the same upper-level vector is disregarded. In this article, introducing conditional generative adversarial network (cGAN), we use only one surrogate model to effectively fit such a set valued mapping, which extracts knowledge from lower-level optimal vectors corresponding to the same upper-level vector. Then, a BLMOP is transformed into a single-level constraint multiobjective optimization problem (CMOP). By adaptively allocating computational resources to optimize the CMOP, promising upper-level vectors are obtained. Furthermore, a lower-level search is executed for these promising upper-level vectors, thus obtaining high-quality solutions. Because of the excellent performance of cGAN and the lower-level search conducted only for promising upper-level vectors, the computational overhead is greatly reduced. The proposed algorithm has achieved the best results in comparison with five state-of-the-art algorithms on benchmark problems and a real-world problem, whose effectiveness has been demonstrated. |
|---|---|
| AbstractList | In bilevel multiobjective optimization problems (BLMOPs), the mapping from an upper-level vector to the corresponding lower-level optimal vectors is a complex set valued mapping. Existing methods require numerous surrogate models to fit such a set valued mapping by grouping the lower-level optimal vectors, and the effects are not satisfactory because the correlation among lower-level optimal vectors corresponding to the same upper-level vector is disregarded. In this article, introducing conditional generative adversarial network (cGAN), we use only one surrogate model to effectively fit such a set valued mapping, which extracts knowledge from lower-level optimal vectors corresponding to the same upper-level vector. Then, a BLMOP is transformed into a single-level constraint multiobjective optimization problem (CMOP). By adaptively allocating computational resources to optimize the CMOP, promising upper-level vectors are obtained. Furthermore, a lower-level search is executed for these promising upper-level vectors, thus obtaining high-quality solutions. Because of the excellent performance of cGAN and the lower-level search conducted only for promising upper-level vectors, the computational overhead is greatly reduced. The proposed algorithm has achieved the best results in comparison with five state-of-the-art algorithms on benchmark problems and a real-world problem, whose effectiveness has been demonstrated. |
| Author | Wang, Weizhong Liu, Hai-Lin |
| Author_xml | – sequence: 1 givenname: Weizhong orcidid: 0009-0000-9891-6516 surname: Wang fullname: Wang, Weizhong email: weizhong-wang@foxmail.com organization: School of Automation, Guangdong University of Technology, Guangzhou, China – sequence: 2 givenname: Hai-Lin orcidid: 0000-0003-2276-1938 surname: Liu fullname: Liu, Hai-Lin email: hlliu@gdut.edu.cn organization: School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou, China |
| BookMark | eNp9kM1OwzAMgCMEEtvgAZA49AU64qQ_yXGrxkAa7DIQtypNPciWtVOaDcHT0247IA6cbNn-LPvrk_OqrpCQG6BDACrvFpPXbMgo40POZBLz5Iz0QEYQUsqS8zanQoZpKt4uSb9pVpRCFIPskXVWV6Xxpq6UDaZYoVPe7DEYlXt0jXKmLT-j_6zdOhyrBstgbCzu0QaTfW13B9B9BU8726bFCvWBnm-92Zhv1bWDkX2vnfEfmytysVS2wetTHJCX-8kiewhn8-ljNpqFmiWJD6HgslAxjVQkVJyyskCpmVCS8VJoSHgRKcp1nGrJQMBSpkwhhSTCGIVMNR-Q9LhXu7ppHC5zbfzhFu-UsTnQvHOWd87yzll-ctaS8IfcOrNpH_yXuT0yBhF_zYOIW__8Bz1SfJs |
| CODEN | ITEVF5 |
| CitedBy_id | crossref_primary_10_1109_TCYB_2024_3492075 |
| Cites_doi | 10.1016/j.omega.2016.01.007 10.1287/opre.1090.0695 10.4236/ajor.2013.32026 10.1109/TCYB.2021.3061551 10.1109/CEC.2012.6256156 10.1287/inte.2013.0700 10.1109/TCYB.2022.3175533 10.1155/2012/102482 10.1109/4235.797969 10.1109/4235.996017 10.1002/atr.1211 10.1109/TEVC.2022.3154057 10.1145/2739480.2754732 10.1145/3520304.3529045 10.1016/j.knosys.2013.07.015 10.1016/j.wre.2015.06.002 10.1080/00207543.2021.1999523 10.1515/cait-2017-0030 10.1371/journal.pone.0128067 10.1007/s11590-015-0918-z 10.1080/09540091.2022.2077312 10.1109/TCYB.2020.2985081 10.1109/TEVC.2003.810761 10.1016/j.cor.2016.08.001 10.1162/evco_a_00015 10.1155/2016/9109824 10.1109/ACCESS.2020.3013568 10.1016/j.knosys.2007.01.003 |
| ContentType | Journal Article |
| DBID | 97E RIA RIE AAYXX CITATION |
| DOI | 10.1109/TEVC.2023.3296536 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science |
| EISSN | 1941-0026 |
| EndPage | 1219 |
| ExternalDocumentID | 10_1109_TEVC_2023_3296536 10185941 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: 62172110 funderid: 10.13039/501100001809 – fundername: Natural Science Foundation of Guangdong Province grantid: 2022A1515010130; 2023A1515011793 funderid: 10.13039/501100003453 |
| GroupedDBID | -~X .DC 0R~ 29I 4.4 5GY 5VS 6IF 6IK 6IL 6IN 97E AAJGR AARMG AASAJ AAWTH ABAZT ABJNI ABQJQ ABVLG ACGFO ACGFS ACIWK ADZIZ AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CHZPO CS3 EBS EJD HZ~ H~9 IEGSK IFIPE IFJZH IPLJI JAVBF LAI M43 O9- OCL P2P PQQKQ RIA RIE RIL RNS TN5 VH1 AAYXX CITATION |
| ID | FETCH-LOGICAL-c266t-1b39ba504a48a572dbe9c28a923d8c163b4a03c57c92181f972ae0164e5e897c3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001328314100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1089-778X |
| IngestDate | Sat Nov 29 03:13:50 EST 2025 Tue Nov 18 22:27:55 EST 2025 Wed Aug 27 02:19:18 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c266t-1b39ba504a48a572dbe9c28a923d8c163b4a03c57c92181f972ae0164e5e897c3 |
| ORCID | 0009-0000-9891-6516 0000-0003-2276-1938 |
| PageCount | 15 |
| ParticipantIDs | ieee_primary_10185941 crossref_primary_10_1109_TEVC_2023_3296536 crossref_citationtrail_10_1109_TEVC_2023_3296536 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-10-01 |
| PublicationDateYYYYMMDD | 2024-10-01 |
| PublicationDate_xml | – month: 10 year: 2024 text: 2024-10-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | IEEE transactions on evolutionary computation |
| PublicationTitleAbbrev | TEVC |
| PublicationYear | 2024 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| References | ref13 ref12 ref15 ref14 Franceschi (ref6); 80 ref31 ref30 ref11 ref33 Bostian (ref7) 2015; 11 ref10 ref2 ref1 ref16 ref19 Zhang (ref29) 2007; 20 ref23 ref26 ref22 Aggarwal (ref25) 2019 ref21 Miyato (ref24) 2018 Sinha (ref17) 2017; 77 ref28 ref27 Tao (ref18) 2012; 2012 ref9 Zhang (ref20) 2013; 53 ref4 ref3 ref5 Kingma (ref32) 2014 Whittaker (ref8) 2017; 66 |
| References_xml | – year: 2019 ident: ref25 article-title: Benchmarking regression methods: A comparison with CGAN publication-title: arXiv:1905.12868 – volume: 66 start-page: 15 year: 2017 ident: ref8 article-title: Spatial targeting of agri-environmental policy using bilevel evolutionary optimization publication-title: Omega doi: 10.1016/j.omega.2016.01.007 – ident: ref1 doi: 10.1287/opre.1090.0695 – ident: ref13 doi: 10.4236/ajor.2013.32026 – ident: ref23 doi: 10.1109/TCYB.2021.3061551 – ident: ref12 doi: 10.1109/CEC.2012.6256156 – ident: ref2 doi: 10.1287/inte.2013.0700 – ident: ref22 doi: 10.1109/TCYB.2022.3175533 – ident: ref19 doi: 10.1155/2012/102482 – ident: ref28 doi: 10.1109/4235.797969 – ident: ref31 doi: 10.1109/4235.996017 – ident: ref3 doi: 10.1002/atr.1211 – ident: ref11 doi: 10.1109/TEVC.2022.3154057 – ident: ref5 doi: 10.1145/2739480.2754732 – volume: 2012 start-page: 359 year: 2012 ident: ref18 article-title: An improved particle swarm optimization for solving bilevel multiobjective programming problem publication-title: J. Appl. Math. – ident: ref30 doi: 10.1145/3520304.3529045 – volume: 53 start-page: 13 year: 2013 ident: ref20 article-title: Solving high dimensional bilevel multiobjective programming problem using a hybrid particle swarm optimization algorithm with crossover operator publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2013.07.015 – volume: 11 start-page: 1 year: 2015 ident: ref7 article-title: Valuing water quality tradeoffs at different spatial scales: An integrated approach using bilevel optimization publication-title: Water Resourc. Econ. doi: 10.1016/j.wre.2015.06.002 – ident: ref21 doi: 10.1080/00207543.2021.1999523 – ident: ref14 doi: 10.1515/cait-2017-0030 – ident: ref4 doi: 10.1371/journal.pone.0128067 – year: 2018 ident: ref24 article-title: cGANs with projection discriminator publication-title: arXiv:1802.05637 – ident: ref9 doi: 10.1007/s11590-015-0918-z – ident: ref27 doi: 10.1080/09540091.2022.2077312 – ident: ref26 doi: 10.1109/TCYB.2020.2985081 – ident: ref33 doi: 10.1109/TEVC.2003.810761 – volume: 77 start-page: 194 year: 2017 ident: ref17 article-title: Approximated set-valued mapping approach for handling multiobjective bilevel problems publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2016.08.001 – ident: ref15 doi: 10.1162/evco_a_00015 – ident: ref10 doi: 10.1155/2016/9109824 – year: 2014 ident: ref32 article-title: Adam: A method for stochastic optimization publication-title: arXiv:1412.6980 – volume: 80 start-page: 1568 volume-title: Proc. 35th Int. Conf. Mach. Learn. ident: ref6 article-title: Bilevel programming for hyperparameter optimization and meta-learning – ident: ref16 doi: 10.1109/ACCESS.2020.3013568 – volume: 20 start-page: 495 issue: 5 year: 2007 ident: ref29 article-title: Decentralized multi-objective bilevel decision making with fuzzy demands publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2007.01.003 |
| SSID | ssj0014519 |
| Score | 2.464937 |
| Snippet | In bilevel multiobjective optimization problems (BLMOPs), the mapping from an upper-level vector to the corresponding lower-level optimal vectors is a complex... |
| SourceID | crossref ieee |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 1205 |
| SubjectTerms | Adaptation models Bilevel optimization Computational modeling conditional generative adversarial network (cGAN) Correlation evolutionary algorithm Generators multiobjective Optimization Sociology Task analysis |
| Title | Conditional Generative Adversarial Network-Based Bilevel Evolutionary Multiobjective Optimization Algorithm |
| URI | https://ieeexplore.ieee.org/document/10185941 |
| Volume | 28 |
| WOSCitedRecordID | wos001328314100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1941-0026 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014519 issn: 1089-778X databaseCode: RIE dateStart: 19970101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07T8MwELagYoCBQimivOSBCSmtk9iNPbZVK6bCUFC3yHYcKLQNSh8S_x6_WrqAxBZFthTlu9xdfPd9B8BdnhGGOI6CtgoNJQfJQOR5GCQRZ4SKSFCR22ETyXBIx2P25MnqlgujlLLNZ6ppLm0tPyvkyhyVtYy6FGGGpr6fJG1H1tqWDIxOiuumZzplpGNfwgwRa436L72mmRPejCPWJlaO-ScI7UxVsUFlUP3n45yAY589wo6D-xTsqXkNVDeTGaD_UGvgaEdm8Ax89ApTmLaHftDpTBsnB-0w5gU3JgiHrh086OqolsGu9hVrNYX9tbdMXn5By9UtxLtzkfBRO5uZZ3HCzvS1KCfLt1kdPA_6o95D4IcsBFLH5mUQipgJThDmmHKSRJlQTEaU68Qvo1JnawJzFEuSSGaygZxpFJXR5VJEUZbI-BxU5sVcXQCIQ6HzJ5knLCNYxzlKsciRzDCLeS5R2ABo89ZT6RXIzSCMaWr_RBBLDVCpASr1QDXA_XbLp5Pf-Gtx3YC0s9Dhc_nL_StwqLdj15p3DSrLcqVuwIFcLyeL8tZa1zeRJs50 |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07T8MwELYQIAEDzyLeeGBCSnESm9gjVEVFQGEoqFtkOw6v0qASKvHv8dkGuoDEFkVOFOW73F18932H0EFZMEEkTaJjEwMlh-hIlWUcZYkUjKtEcVW6YRNZt8v7fXETyOqOC2OMcc1npgmHrpZfVPodtsqOQF2KCaCpzzBKE-LpWt9FA1BK8f30wiaNvB-KmDERR732XasJk8KbaSKOmRNk_glDE3NVXFg5W_rnAy2jxZA_4hMP-AqaMsNVtPQ1mwGHT3UVLUwIDa6h51YFpWm37Ye90jS4OezGMb9JMELc9Q3h0amNawU-td5ibAa4PQ62KUcf2LF1K_XknSS-tu7mJfA48cngvho91g8vDXR71u61OlEYsxBpG53rKFapUJIRKimXLEsKZYROuLSpX8G1zdcUlSTVLNMC8oFSWBwNKHMZZrjIdLqOpofV0GwgTGNlMyhdZqIAgATnVJVEF1SkstQk3kTk663nOmiQwyiMQe7-RYjIAagcgMoDUJvo8PuSVy_A8dfiBoA0sdDjs_XL-X001-ldXeaX592LbTRvb0V9o94Omq5H72YXzepx_fg22nOW9gl2d9G7 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Conditional+Generative+Adversarial+Network-Based+Bilevel+Evolutionary+Multiobjective+Optimization+Algorithm&rft.jtitle=IEEE+transactions+on+evolutionary+computation&rft.au=Wang%2C+Weizhong&rft.au=Liu%2C+Hai-Lin&rft.date=2024-10-01&rft.pub=IEEE&rft.issn=1089-778X&rft.volume=28&rft.issue=5&rft.spage=1205&rft.epage=1219&rft_id=info:doi/10.1109%2FTEVC.2023.3296536&rft.externalDocID=10185941 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1089-778X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1089-778X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1089-778X&client=summon |