DMOEA- \varepsilon \text : Decomposition-Based Multiobjective Evolutionary Algorithm With the \varepsilon -Constraint Framework
Decomposition is an efficient and prevailing strategy for solving multiobjective optimization problems (MOPs). Its success has been witnessed by the multiobjective evolutionary algorithm MOEA/D and its variants. In decomposition-based methods, an MOP is decomposed into a number of scalar subproblems...
Uložené v:
| Vydané v: | IEEE transactions on evolutionary computation Ročník 21; číslo 5; s. 714 - 730 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York
IEEE
01.10.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Predmet: | |
| ISSN: | 1089-778X, 1941-0026 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Decomposition is an efficient and prevailing strategy for solving multiobjective optimization problems (MOPs). Its success has been witnessed by the multiobjective evolutionary algorithm MOEA/D and its variants. In decomposition-based methods, an MOP is decomposed into a number of scalar subproblems by using various scalarizing functions. Most decomposition schemes adopt the weighting method to construct scalarizing functions. In this paper, another classical generation method in the field of mathematical programming, that is the e-constraint method, is adopted for the multiobjective optimization. It selects one of the objectives as the main objective and converts other objectives into constraints. We incorporate the e-constraint method into the decomposition strategy and propose a new decomposition-based multiobjective evolutionary algorithm with the e-constraint framework (DMOEA-εC). It decomposes an MOP into a series of scalar constrained optimization subproblems by assigning each subproblem with an upper bound vector. These subproblems are optimized simultaneously by using information from neighboring subproblems. Besides, a main objective alternation strategy, a solution-to-subproblem matching procedure, and a subproblem-to-solution matching procedure are proposed to strike a balance between convergence and diversity. DMOEA-εC is compared with a number of state-of-theart multiobjective evolutionary algorithms. Experimental studies demonstrate that DMOEA-εC outperforms or performs competitively against these algorithms on the majority of 34 continuous benchmark problems, and it also shows obvious advantages in solving multiobjective 0-1 knapsack problems. |
|---|---|
| AbstractList | Decomposition is an efficient and prevailing strategy for solving multiobjective optimization problems (MOPs). Its success has been witnessed by the multiobjective evolutionary algorithm MOEA/D and its variants. In decomposition-based methods, an MOP is decomposed into a number of scalar subproblems by using various scalarizing functions. Most decomposition schemes adopt the weighting method to construct scalarizing functions. In this paper, another classical generation method in the field of mathematical programming, that is the [Formula Omitted]-constraint method, is adopted for the multiobjective optimization. It selects one of the objectives as the main objective and converts other objectives into constraints. We incorporate the [Formula Omitted]-constraint method into the decomposition strategy and propose a new decomposition-based multiobjective evolutionary algorithm with the [Formula Omitted]-constraint framework (DMOEA-[Formula Omitted]). It decomposes an MOP into a series of scalar constrained optimization subproblems by assigning each subproblem with an upper bound vector. These subproblems are optimized simultaneously by using information from neighboring subproblems. Besides, a main objective alternation strategy, a solution-to-subproblem matching procedure, and a subproblem-to-solution matching procedure are proposed to strike a balance between convergence and diversity. DMOEA-[Formula Omitted] is compared with a number of state-of-the-art multiobjective evolutionary algorithms. Experimental studies demonstrate that DMOEA-[Formula Omitted] outperforms or performs competitively against these algorithms on the majority of 34 continuous benchmark problems, and it also shows obvious advantages in solving multiobjective 0-1 knapsack problems. Decomposition is an efficient and prevailing strategy for solving multiobjective optimization problems (MOPs). Its success has been witnessed by the multiobjective evolutionary algorithm MOEA/D and its variants. In decomposition-based methods, an MOP is decomposed into a number of scalar subproblems by using various scalarizing functions. Most decomposition schemes adopt the weighting method to construct scalarizing functions. In this paper, another classical generation method in the field of mathematical programming, that is the e-constraint method, is adopted for the multiobjective optimization. It selects one of the objectives as the main objective and converts other objectives into constraints. We incorporate the e-constraint method into the decomposition strategy and propose a new decomposition-based multiobjective evolutionary algorithm with the e-constraint framework (DMOEA-εC). It decomposes an MOP into a series of scalar constrained optimization subproblems by assigning each subproblem with an upper bound vector. These subproblems are optimized simultaneously by using information from neighboring subproblems. Besides, a main objective alternation strategy, a solution-to-subproblem matching procedure, and a subproblem-to-solution matching procedure are proposed to strike a balance between convergence and diversity. DMOEA-εC is compared with a number of state-of-theart multiobjective evolutionary algorithms. Experimental studies demonstrate that DMOEA-εC outperforms or performs competitively against these algorithms on the majority of 34 continuous benchmark problems, and it also shows obvious advantages in solving multiobjective 0-1 knapsack problems. |
| Author | Juan Li Jie Chen Bin Xin |
| Author_xml | – sequence: 1 givenname: Jie surname: Chen fullname: Chen, Jie – sequence: 2 givenname: Juan orcidid: 0000-0002-9836-5125 surname: Li fullname: Li, Juan – sequence: 3 givenname: Bin orcidid: 0000-0002-9836-5125 surname: Xin fullname: Xin, Bin |
| BookMark | eNp9kMlOwzAQhi0EEusDIC6WOKd4nMUOt1LKIoG4sB2QIsdMwCWNi-0WOPHqOCpCggOXWTTzz-j_NslqZzskZBfYAICVB9fj29GAMxADXgjICr5CNqDMIGGMF6uxZrJMhJD362TT-wljkOVQbpDP48ur8TChDwvlcOZNazv6EPA90EN6jNpOZ9abYGyXHCmPj_Ry3saunqAOZoF0vLDtvB8r90GH7ZN1JjxP6V2MNDzjr7PJyHY-OGW6QE-cmuKbdS_bZK1Rrced77xFbk7G16Oz5OLq9Hw0vEg0pDJPsJR1k3KsFWjUmDeSZSXkCmQugYsaVQOiKUqd5iqvi7RAzlmdCYYiK1mq0y2yv7w7c_Z1jj5UEzt3XXxZRUxFKjPG87gFyy3trPcOm2rmzDR6q4BVPeeq51z1nKtvzlEj_mi0Capn0ntt_1XuLZUGEX8-iWhJSJF-AXZ5jqE |
| CODEN | ITEVF5 |
| CitedBy_id | crossref_primary_10_1016_j_robot_2025_105106 crossref_primary_10_1109_TCYB_2020_3008697 crossref_primary_10_1080_09540091_2025_2523960 crossref_primary_10_1109_TCYB_2019_2961475 crossref_primary_10_1016_j_ins_2020_05_097 crossref_primary_10_1109_TNNLS_2020_3027293 crossref_primary_10_1016_j_swevo_2021_100866 crossref_primary_10_1016_j_wasman_2019_10_018 crossref_primary_10_1016_j_asoc_2024_111800 crossref_primary_10_1109_TEVC_2024_3418470 crossref_primary_10_1109_TEVC_2020_3047835 crossref_primary_10_1007_s11227_023_05429_3 crossref_primary_10_20965_jaciii_2022_p0570 crossref_primary_10_1109_TEVC_2022_3145582 crossref_primary_10_1016_j_swevo_2020_100815 crossref_primary_10_1007_s40747_024_01656_0 crossref_primary_10_1109_TNSE_2023_3234152 crossref_primary_10_1007_s10489_023_04663_9 crossref_primary_10_1109_ACCESS_2020_3008257 crossref_primary_10_1016_j_knosys_2021_107693 crossref_primary_10_1007_s11432_020_3092_y crossref_primary_10_1016_j_asoc_2017_09_012 crossref_primary_10_1109_ACCESS_2021_3107284 crossref_primary_10_1109_TCYB_2022_3189684 crossref_primary_10_1016_j_swevo_2022_101225 crossref_primary_10_1016_j_swevo_2023_101325 crossref_primary_10_1155_2021_8406864 crossref_primary_10_3233_JIFS_202679 crossref_primary_10_1109_TSMC_2023_3299570 crossref_primary_10_1016_j_ins_2021_11_062 crossref_primary_10_1109_TCYB_2020_2977661 crossref_primary_10_1016_j_eswa_2019_112844 crossref_primary_10_1016_j_neucom_2018_07_080 crossref_primary_10_1016_j_ins_2017_11_052 crossref_primary_10_1016_j_asoc_2022_109430 crossref_primary_10_1109_TCYB_2018_2881227 crossref_primary_10_1016_j_ins_2023_119755 crossref_primary_10_1016_j_swevo_2024_101770 crossref_primary_10_1109_ACCESS_2021_3079152 crossref_primary_10_1016_j_eswa_2018_12_003 crossref_primary_10_1016_j_jhydrol_2019_124431 crossref_primary_10_1109_ACCESS_2019_2910241 crossref_primary_10_1145_3604614 crossref_primary_10_1016_j_swevo_2021_100932 crossref_primary_10_1016_j_eswa_2023_122119 crossref_primary_10_1109_TCYB_2024_3377272 crossref_primary_10_1016_j_asoc_2019_01_033 crossref_primary_10_1016_j_swevo_2021_101020 crossref_primary_10_1016_j_eswa_2022_118915 crossref_primary_10_1016_j_ins_2020_08_070 crossref_primary_10_1016_j_swevo_2024_101683 |
| Cites_doi | 10.1007/s11431-015-6003-0 10.1109/4235.797969 10.1109/ICEC.1994.350037 10.1145/127719.122736 10.1109/CEC.2015.7257280 10.1016/j.advengsoft.2011.05.014 10.1007/978-3-540-31880-4_20 10.1109/TEVC.2003.810758 10.1002/int.20358 10.1007/978-1-4757-5184-0 10.1109/MCDM.2009.4938830 10.1109/CIS.2010.37 10.1109/TSMCB.2012.2231860 10.1016/j.swevo.2011.03.001 10.1007/978-3-642-01020-0_35 10.1109/4235.996017 10.1016/j.future.2013.04.023 10.1109/TSMCA.2009.2012436 10.1080/0305215X.2012.720682 10.1109/TSMCC.2011.2160941 10.1109/TEVC.2013.2281534 10.1109/TEVC.2014.2301794 10.1109/CEC.2003.1299427 10.1109/TSMCB.2006.886164 10.1109/CEC.2009.4982949 10.1016/S0045-7825(99)00389-8 10.1109/TEVC.2009.2033671 10.1109/TEVC.2016.2521868 10.1109/TEVC.2008.925798 10.1162/evco.1994.2.3.221 10.1109/TEVC.2007.892759 10.1109/ICNC.2010.5583335 10.1007/s00500-011-0704-5 10.1162/106365600568202 10.1145/1830483.1830577 10.1109/TEVC.2015.2457616 10.1109/TEVC.2015.2424251 10.1162/EVCO_a_00009 10.1016/j.ejor.2008.10.003 10.1007/978-3-642-15871-1_1 10.1109/TEVC.2010.2051446 10.1109/TEVC.2002.802873 10.1109/CEC.2002.1007032 10.1109/MCI.2006.1597059 10.1109/TEVC.2013.2281535 10.1109/CEC.2012.6256438 10.1016/j.amc.2009.03.037 10.1162/EVCO_a_00038 10.1016/0377-2217(88)90257-3 10.1007/s11432-015-5372-0 10.1162/EVCO_a_00109 10.1016/j.ejor.2013.09.001 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2017 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2017 |
| DBID | 97E RIA RIE AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D |
| DOI | 10.1109/TEVC.2017.2671462 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Technology Research Database |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE/IET Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science |
| EISSN | 1941-0026 |
| EndPage | 730 |
| ExternalDocumentID | 10_1109_TEVC_2017_2671462 7858787 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: 61673058; U1609214; 61304215 funderid: 10.13039/501100001809 – fundername: Research Fund for the Doctoral Program of Higher Education of China grantid: 20131101120033 – fundername: Beijing Outstanding Ph.D. Program Mentor grantid: 20131000704 – fundername: Projects of Major International (Regional) Joint Research Program NSFC grantid: 61120106010 funderid: 10.13039/501100001809 – fundername: Foundation for Innovative Research Groups of the National Natural Science Foundation of China grantid: 61321002 funderid: 10.13039/501100001809 |
| 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 7SC 7SP 8FD JQ2 L7M L~C L~D RIG |
| ID | FETCH-LOGICAL-c1385-e98bf32eba1cece5f804915a1858127beaf17f69c35a5b636e220b470e74903c3 |
| IEDL.DBID | RIE |
| ISSN | 1089-778X |
| IngestDate | Mon Jun 30 07:21:26 EDT 2025 Tue Nov 18 22:24:33 EST 2025 Sat Nov 29 03:13:49 EST 2025 Tue Aug 26 16:43:26 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c1385-e98bf32eba1cece5f804915a1858127beaf17f69c35a5b636e220b470e74903c3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-9836-5125 |
| PQID | 1946384025 |
| PQPubID | 85418 |
| PageCount | 17 |
| ParticipantIDs | proquest_journals_1946384025 ieee_primary_7858787 crossref_primary_10_1109_TEVC_2017_2671462 crossref_citationtrail_10_1109_TEVC_2017_2671462 |
| PublicationCentury | 2000 |
| PublicationDate | 2017-Oct. |
| PublicationDateYYYYMMDD | 2017-10-01 |
| PublicationDate_xml | – month: 10 year: 2017 text: 2017-Oct. |
| PublicationDecade | 2010 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | IEEE transactions on evolutionary computation |
| PublicationTitleAbbrev | TEVC |
| PublicationYear | 2017 |
| 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 ref56 ref59 ref15 ref58 ref14 ref53 ref52 ref55 ref54 ref10 miettinen (ref1) 1999 ref17 ref16 ref19 ref18 ref51 deb (ref2) 2001 mavrotas (ref41) 2013; 219 ref46 ref47 ref42 ref44 zhou (ref57) 2005 ref43 chen (ref27) 2009 ref49 ref8 ref7 zhang (ref60) 2009 ref9 ref4 ref3 ref5 ref40 ref34 ref37 ref36 ref31 ref33 lai (ref30) 2009 ref32 zitzler (ref12) 2004 ref39 ref38 sindhya (ref28) 2011; 15 lü (ref35) 2016; 59 metev (ref48) 2003; 3 haimes (ref45) 1971; 1 zitzler (ref11) 2001 ref24 ref23 ref26 ref25 ref64 ref20 ref63 ref22 ref65 ref21 huband (ref61) 2005 ref29 kukkonen (ref50) 2006 fonseca (ref6) 1993 ref62 |
| References_xml | – volume: 59 start-page: 1 year: 2016 ident: ref35 article-title: A large-scale flight multi-objective assignment approach based on multi-island parallel evolution algorithm with cooperative coevolutionary publication-title: Sci China Inf Sci doi: 10.1007/s11431-015-6003-0 – year: 2001 ident: ref11 article-title: SPEA2: Improving the strength Pareto evolutionary algorithm – ident: ref10 doi: 10.1109/4235.797969 – ident: ref8 doi: 10.1109/ICEC.1994.350037 – ident: ref52 doi: 10.1145/127719.122736 – ident: ref37 doi: 10.1109/CEC.2015.7257280 – year: 1999 ident: ref1 publication-title: Nonlinear Multiobjective Optimization – ident: ref56 doi: 10.1016/j.advengsoft.2011.05.014 – start-page: 280 year: 2005 ident: ref61 article-title: A scalable multi-objective test problem toolkit publication-title: Proc 3rd Int Conf Evol Multi-Criterion Optim doi: 10.1007/978-3-540-31880-4_20 – ident: ref58 doi: 10.1109/TEVC.2003.810758 – ident: ref54 doi: 10.1002/int.20358 – start-page: 209 year: 2009 ident: ref27 article-title: Enhancing MOEA/D with guided mutation and priority update for multi-objective optimization publication-title: Proc IEEE Congr Evol Comput – ident: ref3 doi: 10.1007/978-1-4757-5184-0 – start-page: 553 year: 2006 ident: ref50 article-title: A fast and effective method for pruning of non-dominated solutions in many-objective problems publication-title: Proc Int Conf Parallel Problem Solving Nat – ident: ref55 doi: 10.1109/MCDM.2009.4938830 – ident: ref33 doi: 10.1109/CIS.2010.37 – ident: ref24 doi: 10.1109/TSMCB.2012.2231860 – ident: ref4 doi: 10.1016/j.swevo.2011.03.001 – volume: 219 start-page: 9652 year: 2013 ident: ref41 article-title: An improved version of the augmented $\varepsilon $ -constraint method (AUGMECON2) for finding the exact Pareto set in multi-objective integer programming problems publication-title: Appl Math Comput – ident: ref31 doi: 10.1007/978-3-642-01020-0_35 – ident: ref9 doi: 10.1109/4235.996017 – ident: ref42 doi: 10.1016/j.future.2013.04.023 – ident: ref64 doi: 10.1109/TSMCA.2009.2012436 – ident: ref38 doi: 10.1080/0305215X.2012.720682 – ident: ref65 doi: 10.1109/TSMCC.2011.2160941 – start-page: 416 year: 1993 ident: ref6 article-title: Genetic algorithms for multiobjective optimization: Formulationdiscussion and generalization publication-title: Proc 7th Int Conf Genetic Algorithms – year: 2001 ident: ref2 publication-title: Multi-Objective Optimization Using Evolutionary Algorithms – ident: ref22 doi: 10.1109/TEVC.2013.2281534 – ident: ref49 doi: 10.1109/TEVC.2014.2301794 – volume: 3 start-page: 15 year: 2003 ident: ref48 article-title: A method for nadir point estimation in MOLP problems publication-title: Cybern Inf Technol – ident: ref14 doi: 10.1109/CEC.2003.1299427 – ident: ref51 doi: 10.1109/TSMCB.2006.886164 – ident: ref17 doi: 10.1109/CEC.2009.4982949 – start-page: 832 year: 2004 ident: ref12 article-title: Indicator-based selection in multiobjective search publication-title: Parallel Problem Solving from Nature-PPSN VII – ident: ref44 doi: 10.1016/S0045-7825(99)00389-8 – ident: ref34 doi: 10.1109/TEVC.2009.2033671 – ident: ref53 doi: 10.1109/TEVC.2016.2521868 – ident: ref16 doi: 10.1109/TEVC.2008.925798 – ident: ref7 doi: 10.1162/evco.1994.2.3.221 – ident: ref15 doi: 10.1109/TEVC.2007.892759 – ident: ref29 doi: 10.1109/ICNC.2010.5583335 – year: 2009 ident: ref30 article-title: Multiobjective optimization using MOEA/D with a new mating selection mechanism – volume: 15 start-page: 2041 year: 2011 ident: ref28 article-title: A new hybrid mutation operator for multiobjective optimization with differential evolution publication-title: Soft Comput doi: 10.1007/s00500-011-0704-5 – ident: ref62 doi: 10.1162/106365600568202 – ident: ref32 doi: 10.1145/1830483.1830577 – ident: ref18 doi: 10.1109/TEVC.2015.2457616 – ident: ref19 doi: 10.1109/TEVC.2015.2424251 – ident: ref13 doi: 10.1162/EVCO_a_00009 – start-page: 2568 year: 2005 ident: ref57 article-title: A model-based evolutionary algorithm for bi-objective optimization publication-title: Proc IEEE Congr Evol Comput – ident: ref47 doi: 10.1016/j.ejor.2008.10.003 – ident: ref25 doi: 10.1007/978-3-642-15871-1_1 – year: 2009 ident: ref60 article-title: Multiobjective optimization test instances for the CEC 2009 special session and competition – ident: ref36 doi: 10.1109/TEVC.2010.2051446 – ident: ref63 doi: 10.1109/TEVC.2002.802873 – ident: ref59 doi: 10.1109/CEC.2002.1007032 – ident: ref5 doi: 10.1109/MCI.2006.1597059 – ident: ref21 doi: 10.1109/TEVC.2013.2281535 – ident: ref26 doi: 10.1109/CEC.2012.6256438 – ident: ref40 doi: 10.1016/j.amc.2009.03.037 – ident: ref23 doi: 10.1162/EVCO_a_00038 – ident: ref46 doi: 10.1016/0377-2217(88)90257-3 – ident: ref39 doi: 10.1007/s11432-015-5372-0 – volume: 1 start-page: 296 year: 1971 ident: ref45 article-title: On a bicriterion formulation of the problems of integrated system identification and system optimization publication-title: IEEE Trans Syst Man Cybern – ident: ref20 doi: 10.1162/EVCO_a_00109 – ident: ref43 doi: 10.1016/j.ejor.2013.09.001 |
| SSID | ssj0014519 |
| Score | 2.2142003 |
| Snippet | Decomposition is an efficient and prevailing strategy for solving multiobjective optimization problems (MOPs). Its success has been witnessed by the... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 714 |
| SubjectTerms | decomposition Evolutionary algorithms Evolutionary computation Functions (mathematics) Genetic algorithms Linear programming main objective alternation strategy Matching Mathematical programming Mopping multiobjective optimization Multiple objective analysis Optimization Pareto optimization Sociology solution-to-subproblem matching procedure Strategy subproblem-to-solution matching procedure Upper bound ε-constraint method |
| Title | DMOEA- \varepsilon \text : Decomposition-Based Multiobjective Evolutionary Algorithm With the \varepsilon -Constraint Framework |
| URI | https://ieeexplore.ieee.org/document/7858787 https://www.proquest.com/docview/1946384025 |
| Volume | 21 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE/IET 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/eLvHCXMwlV1LTxsxEB5B1EM5AIUiwqPygROqYd-2ewuQqIcSeoA2B6SV7XghKGSjzUPixF_v2OtERFSVeln5YFuWvvE81t_MAJxgSCK0VIoGaE1porSgksc4CpMis6xFrl0R1x-s2-W9nvi5Bl-XuTDGGEc-M2d26N7y-6We2V9l54ynHAVsHdYZy-pcreWLgS2TUpPpBXqMvOdfMMNAnN-2f11aEhc7izKGmiFasUGuqco7TezMS2fr_w62DZvejSStGvdPsGZGO7C1aNFA_I3dgY039QZ34fXq-qbdouR-LiszngyG5YjcW-oH-UaujGWXewoXvUDj1icuO7dUT7VSJO25l1NZvZDW8KGsBtPHZ_IbvwT9yJVtqe0E6vpPTElnQQD7DHed9u3ld-o7MFAdxjylRnBVxJFRMtRGm7TgGFCEqUQjj44BU0YWISsyoeNUpiqLMxNFgUpYYFgigljHe9AYlSOzD6TfDyKF0Z2SGMGIlKObEGnb8wwlhSdF1IRggUmufXlye8ph7sKUQOQWxtzCmHsYm3C6XDKua3P8a_KuxW050UPWhKMF8Lm_vZM8FAmqJRTj9ODvqw7ho927JvUdQWNazcwxfNDz6WBSfXGC-QfNWuEF |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3Pb9MwFH4aA4lxYLANURjgAyeEN8eJE5tb2VoN0RUOBXqYFNmuw4pKM6VdJU786zw7bsUEQuIS-WAnlr7n9yPv83sALzEkUVYbQxlaU5oZq6iWKY6SrMo9a1HaUMR1UAyHcjxWH7fg9eYujHMukM_ckR-GXP6kttf-V9lxIYVEAbsFt0WWcdbe1trkDHyhlJZOr9BnlOOYw0yYOh71Pp94GldxxPMCdQO_YYVCW5U_dHEwMP3d_9vaA7gfHUnSbZF_CFtuvge76yYNJJ7ZPbj3W8XBffh5ev6h16XkYqUbd7WYzuo5ufDkD_KGnDrPL48kLvoWzduEhPu5tfnWqkXSW0VJ1c0P0p19rZvp8vI7-YJPgp7kjddS3ws0dKBYkv6aAnYAn_q90ckZjT0YqE1SKahT0lQpd0Yn1lknKokhRSI0mnl0DQrjdJUUVa5sKrQweZo7zpnJCuaKTLHUpo9ge17P3WMgkwnjBuM7ozGGUUKio8Ct73qGsiKzineArTEpbSxQ7nc5K0OgwlTpYSw9jGWEsQOvNkuu2uoc_5q873HbTIyQdeBwDXwZz--iTFSGigkFWTz5-6oXcPdsdD4oB--G75_Cjv9OS_E7hO1lc-2ewR27Wk4XzfMgpL8ApQfkTA |
| 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=DMOEA-+%24%5Cvarepsilon+%5Ctext%7BC%7D%24+%3A+Decomposition-Based+Multiobjective+Evolutionary+Algorithm+With+the+%24%5Cvarepsilon+%24+-Constraint+Framework&rft.jtitle=IEEE+transactions+on+evolutionary+computation&rft.au=Chen%2C+Jie&rft.au=Li%2C+Juan&rft.au=Xin%2C+Bin&rft.date=2017-10-01&rft.issn=1089-778X&rft.eissn=1941-0026&rft.volume=21&rft.issue=5&rft.spage=714&rft.epage=730&rft_id=info:doi/10.1109%2FTEVC.2017.2671462&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TEVC_2017_2671462 |
| 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 |