A Self-Adaptive Evolutionary Multi-Task Based Constrained Multi-Objective Evolutionary Algorithm

Constrained multi-objective optimization problems (CMOPs) are difficult to solve since they involve the optimization of multiple objectives and the satisfaction of various constraints. Most constrained multi-objective evolutionary algorithms (CMOEAs) are prone to fall into the local optima due to th...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE transactions on emerging topics in computational intelligence Ročník 7; číslo 4; s. 1 - 15
Hlavní autori:	Qiao, Kangjia, Liang, Jing, Yu, Kunjie, Wang, Minghui, Qu, Boyang, Yue, Caitong, Guo, Yinan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Constrained multi-objective optimization Constraints Evolutionary algorithms evolutionary multi-task optimization Genetic algorithms inter-task intra-task Knowledge transfer Multiple objective analysis Multitasking Optimization Populations self-adaptive Sociology Statistics Task analysis
ISSN:	2471-285X, 2471-285X
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	Constrained multi-objective optimization problems (CMOPs) are difficult to solve since they involve the optimization of multiple objectives and the satisfaction of various constraints. Most constrained multi-objective evolutionary algorithms (CMOEAs) are prone to fall into the local optima due to the imbalance between objectives and constraints as well as the poor search ability of the population. To better solve CMOPs, this paper proposes a double-balanced evolutionary multi-task optimization (DBEMTO) algorithm, which evolves two populations to respectively solve the main task (CMOP) and the auxiliary task (MOP extracted from the CMOP). In DBEMTO, three evolutionary strategies are assigned to each population for offspring generation. The three evolutionary strategies include an individual transfer-based inter-task strategy and two intra-task strategies, not only utilizing the information of inter-task but also providing diverse search abilities of intra-task. Moreover, a self-adaptive scheme is developed to self-adaptively employ three strategies, so that the population can balance the information utilization of both intra-task and inter-task. Then, in the environmental selection, the performance of the three strategies is adopted to guide the sharing of the two offspring populations. Compared with several other state-of-the-art CMOEAs, DBEMTO has performed more competitively according to the final results.
AbstractList	Constrained multi-objective optimization problems (CMOPs) are difficult to solve since they involve the optimization of multiple objectives and the satisfaction of various constraints. Most constrained multi-objective evolutionary algorithms (CMOEAs) are prone to fall into the local optima due to the imbalance between objectives and constraints as well as the poor search ability of the population. To better solve CMOPs, this paper proposes a double-balanced evolutionary multi-task optimization (DBEMTO) algorithm, which evolves two populations to respectively solve the main task (CMOP) and the auxiliary task (MOP extracted from the CMOP). In DBEMTO, three evolutionary strategies are assigned to each population for offspring generation. The three evolutionary strategies include an individual transfer-based inter-task strategy and two intra-task strategies, not only utilizing the information of inter-task but also providing diverse search abilities of intra-task. Moreover, a self-adaptive scheme is developed to self-adaptively employ three strategies, so that the population can balance the information utilization of both intra-task and inter-task. Then, in the environmental selection, the performance of the three strategies is adopted to guide the sharing of the two offspring populations. Compared with several other state-of-the-art CMOEAs, DBEMTO has performed more competitively according to the final results.
Author	Yue, Caitong Qu, Boyang Liang, Jing Guo, Yinan Qiao, Kangjia Yu, Kunjie Wang, Minghui
Author_xml	– sequence: 1 givenname: Kangjia orcidid: 0000-0003-1713-7700 surname: Qiao fullname: Qiao, Kangjia organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou, China – sequence: 2 givenname: Jing orcidid: 0000-0003-0811-0223 surname: Liang fullname: Liang, Jing organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou, China – sequence: 3 givenname: Kunjie orcidid: 0000-0001-9945-1976 surname: Yu fullname: Yu, Kunjie organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou, China – sequence: 4 givenname: Minghui surname: Wang fullname: Wang, Minghui organization: School of Information Engineering, Zhengzhou University, Zhengzhou, China – sequence: 5 givenname: Boyang orcidid: 0000-0001-7539-3927 surname: Qu fullname: Qu, Boyang organization: School of Electrical and Information Engineering, Zhongyuan University of Technology, Zhengzhou, China – sequence: 6 givenname: Caitong orcidid: 0000-0002-3362-0703 surname: Yue fullname: Yue, Caitong organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou, China – sequence: 7 givenname: Yinan orcidid: 0000-0002-4276-5410 surname: Guo fullname: Guo, Yinan organization: China University of Mining and Technology, Beijing, China
BookMark	eNp9kE1Lw0AQhhepYK39A-Ih4Dl1djfZ7B5jqVqo9GAFb3GbTHRrmtTspuC_NzE9FAVP8zIzz3y852RQViUScklhQimom9VsNZ1PGDA-4YwLwfkJGbIgoj6T4cvgSJ-RsbUbAGAqpDwMhuQ19p6wyP040ztn9ujN9lXROFOVuv7yHpvCGX-l7Yd3qy1m3rQqrau1KVvdF5frDaZ_ybh4q2rj3rcX5DTXhcXxIY7I811774O_WN7Pp_HCT5kSzk9zBAGCcpBinUOoGGreJrMgUgIyJlGxNBKZAIWYM5oqkOtMSq1DlBgpPiLX_dxdXX02aF2yqZq6bFcmTAbApYpC2XbJviutK2trzJPUON0d3X1VJBSSztLkx9KkszQ5WNqi7Be6q822_fV_6KqHDCIeAcAZhMC_AbtDhSU
CODEN	ITETCU
CitedBy_id	crossref_primary_10_1016_j_ins_2024_120879 crossref_primary_10_1109_TETCI_2024_3515013 crossref_primary_10_1016_j_swevo_2024_101682 crossref_primary_10_1007_s12293_025_00471_5 crossref_primary_10_1007_s13042_025_02768_w crossref_primary_10_1016_j_eswa_2023_121707 crossref_primary_10_1109_TII_2024_3399909 crossref_primary_10_1016_j_est_2025_115914 crossref_primary_10_1109_JIOT_2024_3366889 crossref_primary_10_1109_TCYB_2025_3531449 crossref_primary_10_1109_TSMC_2024_3427864 crossref_primary_10_1080_0305215X_2024_2335552 crossref_primary_10_1016_j_ins_2024_121214 crossref_primary_10_1016_j_swevo_2025_102006 crossref_primary_10_1109_JAS_2024_124545 crossref_primary_10_1016_j_swevo_2025_102021 crossref_primary_10_1007_s12293_025_00448_4 crossref_primary_10_1016_j_knosys_2024_111998 crossref_primary_10_1016_j_future_2024_04_004 crossref_primary_10_1111_exsy_70046 crossref_primary_10_1007_s10586_025_05169_4 crossref_primary_10_1016_j_swevo_2024_101504 crossref_primary_10_1016_j_swevo_2024_101746 crossref_primary_10_1016_j_swevo_2025_101925 crossref_primary_10_1016_j_swevo_2024_101744 crossref_primary_10_1016_j_swevo_2025_102137 crossref_primary_10_1016_j_eswa_2025_127862 crossref_primary_10_1016_j_ins_2024_121700 crossref_primary_10_1109_JIOT_2024_3395296 crossref_primary_10_1016_j_swevo_2025_102059 crossref_primary_10_1016_j_ins_2023_119547 crossref_primary_10_1109_TETCI_2024_3353615 crossref_primary_10_1016_j_swevo_2025_102070 crossref_primary_10_1016_j_asoc_2024_112560 crossref_primary_10_1109_TEVC_2024_3376729 crossref_primary_10_1016_j_matcom_2024_02_012 crossref_primary_10_1016_j_ins_2024_121033 crossref_primary_10_1016_j_swevo_2025_101937 crossref_primary_10_1109_ACCESS_2024_3354384 crossref_primary_10_1016_j_swevo_2024_101732 crossref_primary_10_1109_TETCI_2023_3313412 crossref_primary_10_1016_j_neucom_2024_129296 crossref_primary_10_1016_j_swevo_2024_101531 crossref_primary_10_3390_math11122680 crossref_primary_10_1109_TEVC_2024_3358854 crossref_primary_10_3390_en17133295 crossref_primary_10_1007_s44336_024_00006_5 crossref_primary_10_3390_computers14080316 crossref_primary_10_1007_s12293_025_00464_4 crossref_primary_10_1007_s43681_025_00759_9 crossref_primary_10_1109_TEVC_2024_3398436 crossref_primary_10_1007_s10462_025_11314_7 crossref_primary_10_1016_j_swevo_2024_101683 crossref_primary_10_1016_j_swevo_2025_101862
Cites_doi	10.1109/TAI.2022.3168038 10.1007/s10462-021-10042-y 10.1016/j.enconman.2020.112509 10.1109/TETCI.2019.2916051 10.1109/TEVC.2013.2281534 10.1109/TCYB.2022.3163759 10.1109/MCI.2017.2742868 10.1109/CEC.2013.6557555 10.1109/CEC.2016.7743992 10.1016/j.eswa.2022.116499 10.1109/TEVC.2022.3160196 10.1109/TEVC.2018.2855411 10.1109/TEVC.2020.2981949 10.1109/TEVC.2022.3155533 10.1109/TEVC.2015.2458037 10.1016/j.ins.2019.10.066 10.1109/TSMC.2019.2943973 10.1109/TEVC.2022.3175065 10.1109/TEVC.2019.2904696 10.1109/4235.797969 10.1109/CEC.2019.8790172 10.1007/s00500-019-03794-x 10.1109/TSMC.2021.3061698 10.1109/TETCI.2017.2769104 10.1109/TEVC.2019.2894743 10.1016/j.asoc.2019.02.041 10.1109/SSCI.2016.7850038 10.1080/0305215X.2016.1271661 10.1109/4235.996017 10.1109/MCI.2022.3155332 10.1109/TEVC.2021.3131236 10.1109/TEVC.2021.3066301 10.1109/TCYB.2015.2461651 10.1109/CEC.2016.7744320 10.1016/j.ins.2015.09.009 10.1080/0305215X.2010.493937 10.1109/TEVC.2021.3100056 10.1109/TEVC.2022.3145582 10.1109/TEVC.2003.810761 10.1109/TCYB.2018.2819208 10.1109/TEVC.2019.2896967 10.1109/4235.873238 10.1016/j.swevo.2021.100961 10.1016/j.asoc.2020.106104 10.1016/j.swevo.2018.08.017 10.1016/j.knosys.2021.107653 10.1109/TEVC.2022.3186667 10.1109/TETCI.2018.2849380 10.1109/TCYB.2020.3031642 10.1109/TEVC.2008.2009032 10.1109/TEVC.2020.3004012 10.1016/j.swevo.2020.100799 10.1016/j.neucom.2017.02.065 10.1109/TCYB.2021.3089633 10.1007/s40747-021-00273-5 10.1007/s40747-016-0011-y
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023
DBID	97E RIA RIE AAYXX CITATION 7SP 8FD L7M
DOI	10.1109/TETCI.2023.3236633
DatabaseName	IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Electronics & Communications Abstracts Technology Research Database Advanced Technologies Database with Aerospace
DatabaseTitle	CrossRef Technology Research Database Advanced Technologies Database with Aerospace Electronics & Communications Abstracts
DatabaseTitleList	Technology Research Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Statistics
EISSN	2471-285X
EndPage	15
ExternalDocumentID	10_1109_TETCI_2023_3236633 10032050
Genre	orig-research
GrantInformation_xml	– fundername: China Postdoctoral Science Foundation grantid: 2020M682347 funderid: 10.13039/501100002858 – fundername: Henan Provincial Young Talents Lifting Project grantid: 2021HYTP007 – fundername: National Natural Science Foundation of China grantid: 62176238; 61806179; 61876169; 61976237 funderid: 10.13039/501100001809 – fundername: National Natural Science Fund for Outstanding Young Scholars of China grantid: 61922072 – fundername: Training Program of Young Backbone teachers in Colleges and universities in Henan Province grantid: 2020GGJS006
GroupedDBID	0R~ 97E AAJGR AASAJ AAWTH ABAZT ABJNI ABQJQ ABVLG ACGFS AGQYO AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS IFIPE JAVBF OCL RIA RIE AAYXX CITATION EJD 7SP 8FD L7M
ID	FETCH-LOGICAL-c296t-cfe060613086bf0592ea3cfed47960d28e92c76d609eef21c908bd88aa5e8e793
IEDL.DBID	RIE
ISICitedReferencesCount	71
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000932866000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2471-285X
IngestDate	Sun Nov 30 04:45:08 EST 2025 Sat Nov 29 05:12:08 EST 2025 Tue Nov 18 22:37:09 EST 2025 Wed Aug 27 02:18:20 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	4
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-c296t-cfe060613086bf0592ea3cfed47960d28e92c76d609eef21c908bd88aa5e8e793
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0001-7539-3927 0000-0002-3362-0703 0000-0003-1713-7700 0000-0003-0811-0223 0000-0002-4276-5410 0000-0001-9945-1976
PQID	2840389758
PQPubID	4437216
PageCount	15
ParticipantIDs	crossref_citationtrail_10_1109_TETCI_2023_3236633 crossref_primary_10_1109_TETCI_2023_3236633 proquest_journals_2840389758 ieee_primary_10032050
PublicationCentury	2000
PublicationDate	2023-08-01
PublicationDateYYYYMMDD	2023-08-01
PublicationDate_xml	– month: 08 year: 2023 text: 2023-08-01 day: 01
PublicationDecade	2020
PublicationPlace	Piscataway
PublicationPlace_xml	– name: Piscataway
PublicationTitle	IEEE transactions on emerging topics in computational intelligence
PublicationTitleAbbrev	TETCI
PublicationYear	2023
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref57 ref12 ref56 ref59 ref14 ref58 ref53 ref11 ref55 ref10 ref54 ref17 ref16 ref19 ref18 ref51 ref50 ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 tian (ref15) 2021 ref49 ref8 ref7 ref9 ref4 ref3 zhang (ref52) 2008 ref6 ref5 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref1 ref39 ref38 ding (ref2) 2019; 3 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29
References_xml	– year: 2021 ident: ref15 article-title: Principled design of translation, scale, and rotation invariant variation operators for metaheuristics – ident: ref43 doi: 10.1109/TAI.2022.3168038 – ident: ref4 doi: 10.1007/s10462-021-10042-y – start-page: 1 year: 2008 ident: ref52 article-title: Multiobjective optimization test instances for the CEC 2009 special session and competition – ident: ref23 doi: 10.1016/j.enconman.2020.112509 – ident: ref11 doi: 10.1109/TETCI.2019.2916051 – ident: ref50 doi: 10.1109/TEVC.2013.2281534 – ident: ref40 doi: 10.1109/TCYB.2022.3163759 – ident: ref54 doi: 10.1109/MCI.2017.2742868 – ident: ref58 doi: 10.1109/CEC.2013.6557555 – ident: ref27 doi: 10.1109/CEC.2016.7743992 – ident: ref39 doi: 10.1016/j.eswa.2022.116499 – ident: ref14 doi: 10.1109/TEVC.2022.3160196 – ident: ref51 doi: 10.1109/TEVC.2018.2855411 – ident: ref53 doi: 10.1109/TEVC.2020.2981949 – ident: ref31 doi: 10.1109/TEVC.2022.3155533 – ident: ref10 doi: 10.1109/TEVC.2015.2458037 – ident: ref25 doi: 10.1016/j.ins.2019.10.066 – ident: ref19 doi: 10.1109/TSMC.2019.2943973 – ident: ref44 doi: 10.1109/TEVC.2022.3175065 – ident: ref24 doi: 10.1109/TEVC.2019.2904696 – ident: ref56 doi: 10.1109/4235.797969 – ident: ref35 doi: 10.1109/CEC.2019.8790172 – ident: ref48 doi: 10.1007/s00500-019-03794-x – ident: ref34 doi: 10.1109/TSMC.2021.3061698 – ident: ref20 doi: 10.1109/TETCI.2017.2769104 – ident: ref17 doi: 10.1109/TEVC.2019.2894743 – ident: ref6 doi: 10.1016/j.asoc.2019.02.041 – ident: ref21 doi: 10.1109/SSCI.2016.7850038 – volume: 3 start-page: 36 year: 2019 ident: ref2 article-title: Dynamic evolutionary multiobjective optimization for raw ore allocation in mineral processing publication-title: IEEE Trans Emerg Topics Comput Intell – ident: ref33 doi: 10.1080/0305215X.2016.1271661 – ident: ref5 doi: 10.1109/4235.996017 – ident: ref28 doi: 10.1109/MCI.2022.3155332 – ident: ref13 doi: 10.1109/TEVC.2021.3131236 – ident: ref7 doi: 10.1109/TEVC.2021.3066301 – ident: ref9 doi: 10.1109/TCYB.2015.2461651 – ident: ref57 doi: 10.1109/CEC.2016.7744320 – ident: ref47 doi: 10.1016/j.ins.2015.09.009 – ident: ref8 doi: 10.1080/0305215X.2010.493937 – ident: ref26 doi: 10.1109/TEVC.2021.3100056 – ident: ref12 doi: 10.1109/TEVC.2022.3145582 – ident: ref55 doi: 10.1109/TEVC.2003.810761 – ident: ref42 doi: 10.1109/TCYB.2018.2819208 – ident: ref49 doi: 10.1109/TEVC.2019.2896967 – ident: ref59 doi: 10.1109/4235.873238 – ident: ref1 doi: 10.1016/j.swevo.2021.100961 – ident: ref38 doi: 10.1016/j.asoc.2020.106104 – ident: ref16 doi: 10.1016/j.swevo.2018.08.017 – ident: ref46 doi: 10.1016/j.knosys.2021.107653 – ident: ref45 doi: 10.1109/TEVC.2022.3186667 – ident: ref3 doi: 10.1109/TETCI.2018.2849380 – ident: ref36 doi: 10.1109/TCYB.2020.3031642 – ident: ref32 doi: 10.1109/TEVC.2008.2009032 – ident: ref18 doi: 10.1109/TEVC.2020.3004012 – ident: ref37 doi: 10.1016/j.swevo.2020.100799 – ident: ref22 doi: 10.1016/j.neucom.2017.02.065 – ident: ref41 doi: 10.1109/TCYB.2021.3089633 – ident: ref29 doi: 10.1007/s40747-021-00273-5 – ident: ref30 doi: 10.1007/s40747-016-0011-y
SSID	ssj0002951354
Score	2.4949124
Snippet	Constrained multi-objective optimization problems (CMOPs) are difficult to solve since they involve the optimization of multiple objectives and the...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	1
SubjectTerms	Constrained multi-objective optimization Constraints Evolutionary algorithms evolutionary multi-task optimization Genetic algorithms inter-task intra-task Knowledge transfer Multiple objective analysis Multitasking Optimization Populations self-adaptive Sociology Statistics Task analysis
Title	A Self-Adaptive Evolutionary Multi-Task Based Constrained Multi-Objective Evolutionary Algorithm
URI	https://ieeexplore.ieee.org/document/10032050 https://www.proquest.com/docview/2840389758
Volume	7
WOSCitedRecordID	wos000932866000001&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: 2471-285X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002951354 issn: 2471-285X databaseCode: RIE dateStart: 20170101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwEA86fNiLnxOnU_rgm2R2adomj1M2FGQKTthbTZOLX3Mb3Qf435uPbiii4Ftoc6XpL5e7S_O7Q-iUQKyobnGsgShME0KxAK6xigmVxk3STDii8E3a67HBgN-VZHXHhQEAd_gMmrbp_uWrsZzbrTKj4bbct43Q19M09WSt1YYKMb5CFNMlMSbk5_1O__K6aeuDNyMSGdMafTM-rprKjyXY2ZXu1j_faBttlg5k0PaI76A1GO2iqvUZfcrlPfTYDu5hqHFbiYldzILOopxfovgIHOMW98X0LbgwFkwFtmSnKxRh2v7mbf7ql8Hvku3h07h4mT2_19BD1wz6CpeFFLAkPJlhqSFMrOE28UuujUNFQETmoqKpCWAUYcCJTBOVhBxAk5bkIcsVY0LEwMBo8D6qjMYjOEBBSLUGbgm4wCiNOYtz4wFxEDriMU_yOmotv3AmyyzjdgzDzEUbIc8cKplFJStRqaOzlczE59j4s3fN4vClp4egjhpLJLNSD6eZMb42g6AJig5_ETtCVft0f6avgSqzYg7HaEMuDGrFiZtin70U0A8
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwEA8yBffi58Tp1D74JpltmnbJ45SNDecUrLC3mjYXv-Y29gX-9yZpNxyi4Ftoc7TpL5e7S_O7Q-icQCCp8jhWQCSmIaFYAFdYBoSm2k1STFiicKfW7bJej9_nZHXLhQEAe_gMqqZp_-XLYTozW2Vaw025bxOhrweUEi-jay23VIj2FvyALqgxLr-MGtF1u2oqhFd94mvj6q-YH1tP5ccibC1Lc_uf77SDtnIX0qlnmO-iNRjsoaLxGrOky_voqe48QF_huhQjs5w5jXk-w8T407GcWxyJybtzpW2YdEzRTlsqQrezm3fJW7YQrkrW-8_D8ev05aOEHpt60C2cl1LAKeHhFKcK3NCYbh3BJEq7VASEry9KWtMhjCQMOElroQxdDqCIl3KXJZIxIQJgoHX4ABUGwwEcIselSgE3FFxglAacBYn2gTgI5fOAh0kZeYsvHKd5nnEzhn5s4w2XxxaV2KAS56iU0cVSZpRl2fizd8ng8K1nBkEZVRZIxrkmTmJtfk0OQR0WHf0idoY2W9FtJ-60uzfHqGielJ3wq6DCdDyDE7SRzjWC41M73b4At-zTVg
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=A+Self-Adaptive+Evolutionary+Multi-Task+Based+Constrained+Multi-Objective+Evolutionary+Algorithm&rft.jtitle=IEEE+transactions+on+emerging+topics+in+computational+intelligence&rft.au=Qiao%2C+Kangjia&rft.au=Liang%2C+Jing&rft.au=Yu%2C+Kunjie&rft.au=Wang%2C+Minghui&rft.date=2023-08-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.eissn=2471-285X&rft.volume=7&rft.issue=4&rft.spage=1098&rft_id=info:doi/10.1109%2FTETCI.2023.3236633&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2471-285X&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2471-285X&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2471-285X&client=summon