Cooperative Differential Evolution With Multiple Populations for Multiobjective Optimization
This paper presents a cooperative differential evolution (DE) with multiple populations for multiobjective optimization. The proposed algorithm has M single-objective optimization subpopulations and an archive population for an M-objective optimization problem. An adaptive DE is applied to each subp...
Saved in:
| Published in: | IEEE transactions on cybernetics Vol. 46; no. 12; pp. 2848 - 2861 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
IEEE
01.12.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 2168-2267, 2168-2275, 2168-2275 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | This paper presents a cooperative differential evolution (DE) with multiple populations for multiobjective optimization. The proposed algorithm has M single-objective optimization subpopulations and an archive population for an M-objective optimization problem. An adaptive DE is applied to each subpopulation to optimize the corresponding objective of the multiobjective optimization problem (MOP). The archive population is also optimized by an adaptive DE. The archive population is used not only to maintain all nondominated solutions found so far but also to guide each subpopulation to search along the whole Pareto front. These (M + 1) populations cooperate to optimize all objectives of the MOP by using adaptive DEs. Simulation results on benchmark problems with two, three, and many objectives show that the proposed algorithm is better than some state-of-the-art multiobjective DE algorithms and other popular multiobjective evolutionary algorithms. The online search behavior and parameter sensitivity of the proposed algorithm are also investigated. |
|---|---|
| AbstractList | This paper presents a cooperative differential evolution (DE) with multiple populations for multiobjective optimization. The proposed algorithm has M single-objective optimization subpopulations and an archive population for an M-objective optimization problem. An adaptive DE is applied to each subpopulation to optimize the corresponding objective of the multiobjective optimization problem (MOP). The archive population is also optimized by an adaptive DE. The archive population is used not only to maintain all nondominated solutions found so far but also to guide each subpopulation to search along the whole Pareto front. These (M + 1) populations cooperate to optimize all objectives of the MOP by using adaptive DEs. Simulation results on benchmark problems with two, three, and many objectives show that the proposed algorithm is better than some state-of-the-art multiobjective DE algorithms and other popular multiobjective evolutionary algorithms. The online search behavior and parameter sensitivity of the proposed algorithm are also investigated. This paper presents a cooperative differential evolution (DE) with multiple populations for multiobjective optimization. The proposed algorithm has M single-objective optimization subpopulations and an archive population for an M -objective optimization problem. An adaptive DE is applied to each subpopulation to optimize the corresponding objective of the multiobjective optimization problem (MOP). The archive population is also optimized by an adaptive DE. The archive population is used not only to maintain all nondominated solutions found so far but also to guide each subpopulation to search along the whole Pareto front. These (M+1) populations cooperate to optimize all objectives of the MOP by using adaptive DEs. Simulation results on benchmark problems with two, three, and many objectives show that the proposed algorithm is better than some state-of-the-art multiobjective DE algorithms and other popular multiobjective evolutionary algorithms. The online search behavior and parameter sensitivity of the proposed algorithm are also investigated.This paper presents a cooperative differential evolution (DE) with multiple populations for multiobjective optimization. The proposed algorithm has M single-objective optimization subpopulations and an archive population for an M -objective optimization problem. An adaptive DE is applied to each subpopulation to optimize the corresponding objective of the multiobjective optimization problem (MOP). The archive population is also optimized by an adaptive DE. The archive population is used not only to maintain all nondominated solutions found so far but also to guide each subpopulation to search along the whole Pareto front. These (M+1) populations cooperate to optimize all objectives of the MOP by using adaptive DEs. Simulation results on benchmark problems with two, three, and many objectives show that the proposed algorithm is better than some state-of-the-art multiobjective DE algorithms and other popular multiobjective evolutionary algorithms. The online search behavior and parameter sensitivity of the proposed algorithm are also investigated. This paper presents a cooperative differential evolution (DE) with multiple populations for multiobjective optimization. The proposed algorithm has [Formula Omitted] single-objective optimization subpopulations and an archive population for an [Formula Omitted]-objective optimization problem. An adaptive DE is applied to each subpopulation to optimize the corresponding objective of the multiobjective optimization problem (MOP). The archive population is also optimized by an adaptive DE. The archive population is used not only to maintain all nondominated solutions found so far but also to guide each subpopulation to search along the whole Pareto front. These [Formula Omitted] populations cooperate to optimize all objectives of the MOP by using adaptive DEs. Simulation results on benchmark problems with two, three, and many objectives show that the proposed algorithm is better than some state-of-the-art multiobjective DE algorithms and other popular multiobjective evolutionary algorithms. The online search behavior and parameter sensitivity of the proposed algorithm are also investigated. |
| Author | Jun Zhang Weiwei Zhang Jiahai Wang |
| Author_xml | – sequence: 1 givenname: Jiahai surname: Wang fullname: Wang, Jiahai – sequence: 2 givenname: Weiwei surname: Zhang fullname: Zhang, Weiwei – sequence: 3 givenname: Jun surname: Zhang fullname: Zhang, Jun |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26552101$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kU9rFTEUxYNUbK39ACLIgBs375mbZJKZpT7bKlTqoiKCMGSSG8wjbzJNMgX99M7741t0YTY35P7OyeWe5-RkiAMS8hLoEoC27-5WPz4sGYV6yURLpWyfkDMGslkwpuqT412qU3KR85rOp5mf2uYZOWWyrhlQOCM_VzGOmHTxD1h99M5hwqF4HarLhxim4uNQffflV_VlCsWPAauvcZyC3jZy5WLaN2K_RrPzuB2L3_g_O-AFeep0yHhxqOfk29Xl3erT4ub2-vPq_c3CCGjLAqRprGAAzAltem21aK2yWglGlaUtqNoY2yvKHbdaC0cFgoVaWN1b7pCfk7d73zHF-wlz6TY-GwxBDxin3EHDpORMNWpG3zxC13FKwzzdTAnGgEMjZ-r1gZr6DdpuTH6j0-_u395mAPaASTHnhO6IAO228XTbeLptPN0hnlmjHmmML7s9laR9-K_y1V7pEfH4k-IgWi74XxRSnaU |
| CODEN | ITCEB8 |
| CitedBy_id | crossref_primary_10_1109_TFUZZ_2024_3397728 crossref_primary_10_1109_ACCESS_2024_3419849 crossref_primary_10_1109_TEVC_2017_2767023 crossref_primary_10_1016_j_ins_2023_02_043 crossref_primary_10_1109_TNNLS_2020_3027298 crossref_primary_10_1109_ACCESS_2019_2911087 crossref_primary_10_1016_j_energy_2021_123083 crossref_primary_10_1109_TCYB_2018_2834466 crossref_primary_10_1109_ACCESS_2018_2852640 crossref_primary_10_1109_TFUZZ_2018_2872125 crossref_primary_10_1016_j_ins_2017_11_030 crossref_primary_10_1007_s12065_021_00567_0 crossref_primary_10_1109_TEVC_2022_3225632 crossref_primary_10_1016_j_swevo_2019_05_007 crossref_primary_10_1109_TCYB_2018_2866527 crossref_primary_10_1007_s12293_021_00336_7 crossref_primary_10_1109_ACCESS_2023_3300590 crossref_primary_10_1109_TSMC_2023_3298690 crossref_primary_10_1016_j_neucom_2018_02_038 crossref_primary_10_3233_ICA_180594 crossref_primary_10_1109_TII_2019_2961340 crossref_primary_10_1587_nolta_15_404 crossref_primary_10_3390_ma18051159 crossref_primary_10_1016_j_asoc_2025_113762 crossref_primary_10_1002_cpe_8221 crossref_primary_10_1109_TSMC_2016_2631479 crossref_primary_10_1109_TSMC_2023_3298804 crossref_primary_10_1109_TCYB_2018_2868493 crossref_primary_10_1109_TSMC_2020_2964797 crossref_primary_10_1007_s10489_020_01733_0 crossref_primary_10_1007_s10462_021_10042_y crossref_primary_10_1016_j_swevo_2018_02_011 crossref_primary_10_1109_TEVC_2021_3131124 crossref_primary_10_1007_s00202_023_01988_z crossref_primary_10_1016_j_asoc_2021_108297 crossref_primary_10_1016_j_swevo_2022_101142 crossref_primary_10_1007_s00500_019_03934_3 crossref_primary_10_1007_s10710_017_9305_0 crossref_primary_10_1016_j_swevo_2020_100697 crossref_primary_10_1038_s41598_024_76877_x crossref_primary_10_1109_TCYB_2017_2780274 crossref_primary_10_1109_TCYB_2018_2819208 crossref_primary_10_1109_TSMC_2018_2875043 crossref_primary_10_1016_j_cie_2021_107367 crossref_primary_10_1016_j_engappai_2020_103479 crossref_primary_10_1016_j_jocs_2022_101746 crossref_primary_10_1016_j_swevo_2019_02_004 crossref_primary_10_1016_j_knosys_2018_01_028 crossref_primary_10_1109_TCYB_2018_2811761 crossref_primary_10_1109_TBDATA_2017_2685581 crossref_primary_10_1007_s00500_019_04328_1 crossref_primary_10_1109_TCYB_2018_2849343 crossref_primary_10_1016_j_eswa_2025_128163 crossref_primary_10_1109_ACCESS_2021_3100483 crossref_primary_10_1016_j_ins_2024_120185 crossref_primary_10_1016_j_jpowsour_2021_230473 crossref_primary_10_1016_j_cor_2021_105543 crossref_primary_10_1109_TCYB_2019_2935762 crossref_primary_10_1109_TFUZZ_2022_3163909 crossref_primary_10_1016_j_ins_2019_06_051 crossref_primary_10_1109_TSMC_2018_2861879 crossref_primary_10_1016_j_ijepes_2023_109584 crossref_primary_10_1016_j_swevo_2024_101771 crossref_primary_10_1109_TII_2018_2884951 crossref_primary_10_1016_j_swevo_2021_100960 crossref_primary_10_1016_j_future_2017_10_015 crossref_primary_10_1016_j_ins_2020_11_030 crossref_primary_10_1016_j_asoc_2018_08_020 crossref_primary_10_3390_pr12071531 crossref_primary_10_1109_TSMC_2019_2956121 crossref_primary_10_1109_TCYB_2017_2685944 crossref_primary_10_1109_TII_2019_2952565 crossref_primary_10_1109_ACCESS_2017_2702561 crossref_primary_10_1016_j_ins_2018_11_015 crossref_primary_10_1016_j_ins_2020_02_027 crossref_primary_10_1016_j_future_2019_02_030 crossref_primary_10_1016_j_ins_2025_122671 crossref_primary_10_3390_app12115524 crossref_primary_10_1093_jcde_qwac127 crossref_primary_10_1016_j_seta_2021_101938 crossref_primary_10_1109_TEVC_2016_2606577 crossref_primary_10_1016_j_asoc_2020_106393 crossref_primary_10_1016_j_asoc_2017_09_039 crossref_primary_10_1109_TCYB_2022_3180214 crossref_primary_10_3390_sym14102037 crossref_primary_10_1109_TEVC_2024_3365814 crossref_primary_10_3233_JIFS_202810 crossref_primary_10_1016_j_ins_2018_06_063 |
| Cites_doi | 10.1109/TSMCC.2008.923864 10.1109/TEVC.2005.860762 10.1109/TEVC.2015.2424251 10.1007/s00500-012-0816-6 10.1109/TSMCB.2012.2209115 10.1016/j.swevo.2011.03.001 10.1109/TEVC.2007.892759 10.1109/TEVC.2008.920671 10.1007/978-3-540-68830-3_7 10.1109/4235.996017 10.1016/S0893-6080(02)00095-3 10.1109/TEVC.2008.925798 10.1007/s00500-008-0394-9 10.1016/j.asoc.2014.06.011 10.1109/TEVC.2013.2281543 10.1109/TEVC.2010.2059031 10.1109/TSMCC.2011.2160941 10.1023/A:1008202821328 10.1016/j.ins.2015.05.026 10.1109/TEVC.2005.851274 10.1109/TSMCB.2008.925757 10.1016/j.ejor.2009.05.005 10.1109/TCYB.2014.2316552 10.1007/s10462-009-9137-2 10.1016/j.asoc.2015.04.061 10.1109/CEC.2001.934295 10.1109/SDE.2013.6601435 10.1109/CEC.2003.1299614 10.1109/TEVC.2006.872133 10.1007/s10462-012-9378-3 10.1109/TEVC.2010.2058120 10.1016/j.ins.2013.06.011 10.1109/TCYB.2014.2334692 10.1109/TEVC.2013.2281535 10.1109/TEVC.2013.2262178 10.1109/TEVC.2009.2014613 10.1109/CEC.2013.6557903 10.1109/TEVC.2005.861417 10.1016/j.swevo.2011.02.002 10.1007/s00500-008-0323-y 10.1109/TEVC.2003.810758 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2016 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2016 |
| DBID | 97E ESBDL RIA RIE AAYXX CITATION NPM 7SC 7SP 7TB 8FD F28 FR3 H8D JQ2 L7M L~C L~D 7X8 |
| DOI | 10.1109/TCYB.2015.2490669 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE/IET Electronic Library (IEL) (UW System Shared) CrossRef PubMed Computer and Information Systems Abstracts Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional MEDLINE - Academic |
| DatabaseTitle | CrossRef PubMed Aerospace Database Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Computer and Information Systems Abstracts Professional MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic PubMed Aerospace Database |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE/IET Electronic Library (IEL) (UW System Shared) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Statistics Sciences (General) |
| EISSN | 2168-2275 |
| EndPage | 2861 |
| ExternalDocumentID | 26552101 10_1109_TCYB_2015_2490669 7314934 |
| Genre | orig-research Journal Article |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China for Distinguished Young Scholars grantid: 61125205 funderid: 10.13039/501100001809 – fundername: National High-Technology Research and Development Program (863 Program) of China grantid: 2013AA01A212 – fundername: National Natural Science Foundation of China grantid: 61332002; 61300044 funderid: 10.13039/501100001809 |
| GroupedDBID | 0R~ 4.4 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACIWK AENEX AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD ESBDL HZ~ IFIPE IPLJI JAVBF M43 O9- OCL PQQKQ RIA RIE RNS AAYXX CITATION NPM RIG 7SC 7SP 7TB 8FD F28 FR3 H8D JQ2 L7M L~C L~D 7X8 |
| ID | FETCH-LOGICAL-c419t-16c8d42112f4acbada49d7da74207d09175ccdb703f3daa4f04e1d154dabd3fe3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 99 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000388923100014&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2168-2267 2168-2275 |
| IngestDate | Thu Oct 02 11:52:56 EDT 2025 Sun Jun 29 15:41:52 EDT 2025 Thu Apr 03 07:02:56 EDT 2025 Tue Nov 18 21:46:37 EST 2025 Sat Nov 29 06:48:31 EST 2025 Tue Aug 26 16:43:01 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 12 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/OAPA.html |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c419t-16c8d42112f4acbada49d7da74207d09175ccdb703f3daa4f04e1d154dabd3fe3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://ieeexplore.ieee.org/document/7314934 |
| PMID | 26552101 |
| PQID | 1842213186 |
| PQPubID | 85422 |
| PageCount | 14 |
| ParticipantIDs | proquest_miscellaneous_1826632787 ieee_primary_7314934 proquest_journals_1842213186 crossref_primary_10_1109_TCYB_2015_2490669 pubmed_primary_26552101 crossref_citationtrail_10_1109_TCYB_2015_2490669 |
| PublicationCentury | 2000 |
| PublicationDate | 2016-12-01 |
| PublicationDateYYYYMMDD | 2016-12-01 |
| PublicationDate_xml | – month: 12 year: 2016 text: 2016-12-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: Piscataway |
| PublicationTitle | IEEE transactions on cybernetics |
| PublicationTitleAbbrev | TCYB |
| PublicationTitleAlternate | IEEE Trans Cybern |
| PublicationYear | 2016 |
| 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 ref12 ref14 ref11 ref10 zitzler (ref37) 2002 ref17 ref16 ref19 ref18 zhang (ref39) 2009 ref50 knowles (ref42) 2006 ref46 ref45 ref48 ref47 ref41 ref44 ref43 ref49 ref7 ref9 ref4 ref3 ref6 ref40 dorronsoro (ref31) 2011; 362 ref36 ref30 ref33 ref32 ref2 ref1 ref38 cheng (ref5) 2013; 1 chiang (ref15) 2013 parsopoulos (ref35) 2004 ali (ref8) 2012; 217 ref24 ref23 iorio (ref27) 2004 ref26 ref25 ref20 schaffer (ref34) 1985 ref22 ref21 ref28 goh (ref29) 2009; 13 |
| References_xml | – ident: ref22 doi: 10.1109/TSMCC.2008.923864 – ident: ref28 doi: 10.1109/TEVC.2005.860762 – ident: ref47 doi: 10.1109/TEVC.2015.2424251 – start-page: 1 year: 2009 ident: ref39 article-title: Multiobjective optimization test instances for the CEC 2009 special session and competition publication-title: Proc IEEE Congr Evol Comput – ident: ref11 doi: 10.1007/s00500-012-0816-6 – ident: ref12 doi: 10.1109/TSMCB.2012.2209115 – start-page: 537 year: 2004 ident: ref27 publication-title: A Cooperative Coevolutionary Multiobjective Algorithm Using Non-Dominated Sorting – start-page: 93 year: 1985 ident: ref34 article-title: Multiple objective optimization with vector evaluated genetic algorithms publication-title: Proc 1st Int Conf Genet Algorithms – ident: ref1 doi: 10.1016/j.swevo.2011.03.001 – ident: ref3 doi: 10.1109/TEVC.2007.892759 – volume: 1 start-page: 1 year: 2013 ident: ref5 article-title: Multi-objective differential evolution: A recent survey publication-title: Applied Soft Computing – volume: 13 start-page: 103 year: 2009 ident: ref29 article-title: A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2008.920671 – ident: ref4 doi: 10.1007/978-3-540-68830-3_7 – ident: ref2 doi: 10.1109/4235.996017 – ident: ref25 doi: 10.1016/S0893-6080(02)00095-3 – ident: ref10 doi: 10.1109/TEVC.2008.925798 – ident: ref9 doi: 10.1007/s00500-008-0394-9 – ident: ref32 doi: 10.1016/j.asoc.2014.06.011 – start-page: 823 year: 2004 ident: ref35 article-title: Multiobjective optimization using parallel vector evaluated particle swarm optimization publication-title: Proc IASTED Int Conf Artif Intell Appl – ident: ref50 doi: 10.1109/TEVC.2013.2281543 – ident: ref13 doi: 10.1109/TEVC.2010.2059031 – ident: ref21 doi: 10.1109/TSMCC.2011.2160941 – ident: ref6 doi: 10.1023/A:1008202821328 – ident: ref19 doi: 10.1016/j.ins.2015.05.026 – ident: ref43 doi: 10.1109/TEVC.2005.851274 – ident: ref46 doi: 10.1109/TSMCB.2008.925757 – ident: ref30 doi: 10.1016/j.ejor.2009.05.005 – ident: ref18 doi: 10.1109/TCYB.2014.2316552 – ident: ref14 doi: 10.1007/s10462-009-9137-2 – volume: 217 start-page: 404 year: 2012 ident: ref8 article-title: An efficient differential evolution based algorithm for solving multi-objective optimization problems publication-title: Eur J Oper Res – ident: ref24 doi: 10.1016/j.asoc.2015.04.061 – ident: ref7 doi: 10.1109/CEC.2001.934295 – start-page: 1 year: 2013 ident: ref15 article-title: Parameter control mechanisms in differential evolution: A tutorial review and taxonomy publication-title: Proc IEEE Symp Differ Evol (SDE) doi: 10.1109/SDE.2013.6601435 – ident: ref26 doi: 10.1109/CEC.2003.1299614 – ident: ref17 doi: 10.1109/TEVC.2006.872133 – ident: ref23 doi: 10.1007/s10462-012-9378-3 – ident: ref48 doi: 10.1109/TEVC.2010.2058120 – volume: 362 start-page: 49 year: 2011 ident: ref31 publication-title: Multi-Objective Cooperative Coevolutionary Evolutionary Algorithms for Continuous and Combinatorial Optimization – ident: ref20 doi: 10.1016/j.ins.2013.06.011 – ident: ref49 doi: 10.1109/TCYB.2014.2334692 – year: 2006 ident: ref42 article-title: A tutorial on the performance assessment of stochastic multiobjective optimizers – ident: ref38 doi: 10.1109/TEVC.2013.2281535 – ident: ref36 doi: 10.1109/TEVC.2013.2262178 – ident: ref16 doi: 10.1109/TEVC.2009.2014613 – ident: ref33 doi: 10.1109/CEC.2013.6557903 – start-page: 95 year: 2002 ident: ref37 article-title: SPEA2: Improving the strength Pareto evolutionary algorithm publication-title: Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems – ident: ref40 doi: 10.1109/TEVC.2005.861417 – ident: ref44 doi: 10.1016/j.swevo.2011.02.002 – ident: ref45 doi: 10.1007/s00500-008-0323-y – ident: ref41 doi: 10.1109/TEVC.2003.810758 |
| SSID | ssj0000816898 |
| Score | 2.4573624 |
| Snippet | This paper presents a cooperative differential evolution (DE) with multiple populations for multiobjective optimization. The proposed algorithm has M... This paper presents a cooperative differential evolution (DE) with multiple populations for multiobjective optimization. The proposed algorithm has [Formula... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 2848 |
| SubjectTerms | Algorithms Approximation methods Archive search Archives & records Computer simulation cooperative populations Customer relationship management differential evolution (DE) Estimation Evolutionary algorithms Evolutionary computation Frequency modulation many-objective optimization Mathematical programming multiobjective optimization Multiple objective analysis Optimization Parameter sensitivity Pareto optimization Populations Sociology Statistics |
| Title | Cooperative Differential Evolution With Multiple Populations for Multiobjective Optimization |
| URI | https://ieeexplore.ieee.org/document/7314934 https://www.ncbi.nlm.nih.gov/pubmed/26552101 https://www.proquest.com/docview/1842213186 https://www.proquest.com/docview/1826632787 |
| Volume | 46 |
| WOSCitedRecordID | wos000388923100014&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/IET Electronic Library (IEL) (UW System Shared) customDbUrl: eissn: 2168-2275 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816898 issn: 2168-2267 databaseCode: RIE dateStart: 20130101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3JTuQwEC0B4sBl2IewySNxgBGBLE4cH6EBcRiWA4hGQoq8RYCgg-huvp_y0jkNSNyc2EmsVLnqlZdXADulRh9VUR5beqqYpkrGXGptL4Vo8F5mHGX-P3Z5WfX7_HoK9ruzMMYYt_nMHNiiW8vXrRrbqbJDliOez-k0TDNW-rNa3XyKSyDhUt9mWIgRVbCwiJkm_PCmd39s93EVBxhuoJe1ZKFZWaDvCtlgJh7JpVj5Gm06r3M2_7P-LsCvgC7JkVeHRZgygyVYDON3SHYDyfTeEsxZlOlJmpfhode2b8ZzgJOTkDIFh_4LOf0IqknunkaP5CJsPyTXXd6vIUHY6yta-ezNJ7lCQ_QaTniuwO3Z6U3vPA5pF2JFUz6K01JVmmJgmDVUKCm0oFwzLTCITphGfMEKpbREU9HkWgjaJNSkGqGYFlLnjclXYWbQDswaEEvtk3OaiqYwVMqEG5aXqdSVsMuBRRZBMvn1tQqc5DY1xkvtYpOE11ZwtRVcHQQXwd_ukTdPyPFd42Urla5hEEgEmxP51mHIDmsMdbMsRRNXRvCnq8bBZldQxMC0Y9sG8UyeoZGL4LfXi-7dE3Va__83N2AOe1b6nTCbMDN6H5stmFUfKOv3bdTofrXtNPoTiIvvpQ |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT9wwEB5RilQutDxa0vIwUg9QEUgcJ1kfywICsWw5LCpISJFfESDYIHaX39_xY3OiSNyc2EmszHjmGz--AfhZaPRRHcZjS08Vs1TJmEut7aUQNd6jxlHm98p-v3N1xS9mYLc9C2OMcZvPzJ4turV83aiJnSrbLzPE8xn7AB9zxmjiT2u1MyouhYRLfkuxECOuKMMyZprw_UH3-sDu5Mr3MOBAP2vpQmmRo_cK-WCmPsklWfk_3nR-5_jz-3r8BRYCviS_vUIswowZLsFiGMEjsh1opneWYN7iTE_TvAw33aZ5Mp4FnByGpCk4-B_I0UtQTvL3bnxLzsMGRHLRZv4aEQS-vqKR996Akj9oih7DGc8VuDw-GnRP4pB4IVYs5eM4LVRHMwwNac2EkkILxnWpBYbRSakRYZS5UlqisagzLQSrE2ZSjWBMC6mz2mRfYXbYDM0qEEvuk3GWijo3TMqEmzIrUqk7wi4I5jSCZPrrKxVYyW1yjIfKRScJr6zgKiu4Kggugl_tI0-ekuOtxstWKm3DIJAI1qbyrcKgHVUY7FKaopErIthqq3G42TUUMTTNxLZBRJNRNHMRfPN60b57qk7fX__mJnw6GZz3qt5p_-wHzGMvC78vZg1mx88Tsw5z6gXl_rzh9PoffTXyBA |
| 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=Cooperative+Differential+Evolution+With+Multiple+Populations+for+Multiobjective+Optimization&rft.jtitle=IEEE+transactions+on+cybernetics&rft.au=Wang%2C+Jiahai&rft.au=Zhang%2C+Weiwei&rft.au=Zhang%2C+Jun&rft.date=2016-12-01&rft.eissn=2168-2275&rft.volume=46&rft.issue=12&rft.spage=2848&rft_id=info:doi/10.1109%2FTCYB.2015.2490669&rft_id=info%3Apmid%2F26552101&rft.externalDocID=26552101 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2168-2267&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2168-2267&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2168-2267&client=summon |