Constrained Subproblems in a Decomposition-Based Multiobjective Evolutionary Algorithm

A decomposition approach decomposes a multiobjective optimization problem into a number of scalar objective optimization subproblems. It plays a key role in decomposition-based multiobjective evolutionary algorithms. However, many widely used decomposition approaches, originally proposed for mathema...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transactions on evolutionary computation Ročník 20; číslo 3; s. 475 - 480
Hlavní autoři:	Wang, Luping, Zhang, Qingfu, Zhou, Aimin, Gong, Maoguo, Jiao, Licheng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.06.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms constraint Constraints Convergence De-composition approach Decomposition Evolutionary algorithms Evolutionary computation Evolutionary multiobjective optimization Linear programming Measurement Optimization Scalars Sociology Statistics Strategy Constraint evolutionary multiobjective optimization decomposition approach
ISSN:	1089-778X, 1941-0026
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	A decomposition approach decomposes a multiobjective optimization problem into a number of scalar objective optimization subproblems. It plays a key role in decomposition-based multiobjective evolutionary algorithms. However, many widely used decomposition approaches, originally proposed for mathematical programming algorithms, may not be very suitable for evolutionary algorithms. To help decomposition-based multiobjective evolutionary algorithms balance the population diversity and convergence in an appropriate manner, this letter proposes to impose some constraints on the subproblems. Experiments have been conducted to demonstrate that our proposed constrained decomposition approach works well on most test instances. We further propose a strategy for adaptively adjusting constraints by using information collected from the search. Experimental results show that it can significantly improve the algorithm performance.
AbstractList	A decomposition approach decomposes a multiobjective optimization problem into a number of scalar objective optimization subproblems. It plays a key role in decomposition-based multiobjective evolutionary algorithms. However, many widely used decomposition approaches, originally proposed for mathematical programming algorithms, may not be very suitable for evolutionary algorithms. To help decomposition-based multiobjective evolutionary algorithms balance the population diversity and convergence in an appropriate manner, this letter proposes to impose some constraints on the subproblems. Experiments have been conducted to demonstrate that our proposed constrained decomposition approach works well on most test instances. We further propose a strategy for adaptively adjusting constraints by using information collected from the search. Experimental results show that it can significantly improve the algorithm performance.
Author	Maoguo Gong Aimin Zhou Licheng Jiao Luping Wang Qingfu Zhang
Author_xml	– sequence: 1 givenname: Luping surname: Wang fullname: Wang, Luping – sequence: 2 givenname: Qingfu surname: Zhang fullname: Zhang, Qingfu – sequence: 3 givenname: Aimin surname: Zhou fullname: Zhou, Aimin – sequence: 4 givenname: Maoguo surname: Gong fullname: Gong, Maoguo – sequence: 5 givenname: Licheng surname: Jiao fullname: Jiao, Licheng
BookMark	eNp9kD1PwzAQhi1UJCjwAxBLJBaWFJ_z4XgsoXxIRQx8iM1ynAu4SuISJ5X49zhqxdCByWfd857unimZtLZFQs6BzgCouH5dvOczRiGZsTjhKaQH5BhEDCGlLJ34mmYi5Dz7OCJT51aUQpyAOCbvuW1d3ynTYhm8DMW6s0WNjQtMG6jgFrVt1taZ3tg2vFHOQ09D7X_FCnVvNhgsNrYexrbqfoJ5_Wk70381p-SwUrXDs917Qt7uFq_5Q7h8vn_M58tQR4L1YYq6Sosiy1AojAF4CVoxQFFqFHGWJJTqNFUcyoohLWnJI-VZ364SjKokOiFX27l-7-8BXS8b4zTWtWrRDk5CxpJYUK_Eo5d76MoOXeu3k8AF44JnFDzFt5TurHMdVlKbXo33jZJqCVSOvuXoW46-5c63T8Ject2Zxlv5N3OxzRhE_OM5pJQzHv0Cq9yPBg
CODEN	ITEVF5
CitedBy_id	crossref_primary_10_1007_s10586_019_03022_z crossref_primary_10_1109_TCYB_2020_3008697 crossref_primary_10_1155_2018_1753071 crossref_primary_10_1016_j_asoc_2018_10_028 crossref_primary_10_1007_s00500_021_06264_5 crossref_primary_10_1109_TEVC_2016_2598858 crossref_primary_10_1016_j_swevo_2021_100940 crossref_primary_10_1016_j_asoc_2023_110796 crossref_primary_10_1016_j_asoc_2024_111807 crossref_primary_10_3389_fenrg_2022_928744 crossref_primary_10_1109_TEVC_2016_2608507 crossref_primary_10_1016_j_ins_2021_05_080 crossref_primary_10_1109_ACCESS_2018_2873401 crossref_primary_10_1016_j_asoc_2024_111881 crossref_primary_10_1016_j_trit_2016_11_002 crossref_primary_10_3390_s20082230 crossref_primary_10_3390_e27050524 crossref_primary_10_1016_j_swevo_2024_101503 crossref_primary_10_1109_ACCESS_2020_2973670 crossref_primary_10_1016_j_neucom_2020_01_114 crossref_primary_10_1061_JAEEEZ_ASENG_5155 crossref_primary_10_1016_j_asoc_2022_108938 crossref_primary_10_3390_a10030086 crossref_primary_10_1016_j_asoc_2018_06_023 crossref_primary_10_1016_j_swevo_2019_02_003 crossref_primary_10_3390_en13051231 crossref_primary_10_1109_TEVC_2016_2519378 crossref_primary_10_1109_TEVC_2017_2707980 crossref_primary_10_1007_s10489_020_01969_w crossref_primary_10_1016_j_asoc_2024_112434 crossref_primary_10_1016_j_swevo_2021_100886 crossref_primary_10_1016_j_ins_2022_10_136 crossref_primary_10_1016_j_engappai_2022_105249 crossref_primary_10_1109_TEVC_2016_2611642 crossref_primary_10_1016_j_engappai_2023_107564 crossref_primary_10_1109_TEVC_2017_2690446 crossref_primary_10_1016_j_ins_2020_03_093 crossref_primary_10_1007_s00521_018_3563_5 crossref_primary_10_3390_sym13010136 crossref_primary_10_1162_evco_a_00269 crossref_primary_10_1109_TEVC_2016_2521868 crossref_primary_10_1007_s00500_016_2076_3 crossref_primary_10_1109_TSMC_2023_3324797 crossref_primary_10_1016_j_swevo_2019_02_010 crossref_primary_10_1109_TSMC_2019_2931636 crossref_primary_10_1016_j_eswa_2022_118915 crossref_primary_10_1080_21681015_2020_1735544 crossref_primary_10_1016_j_asoc_2020_107002 crossref_primary_10_1016_j_ins_2019_06_001 crossref_primary_10_1016_j_neucom_2019_02_002 crossref_primary_10_1109_TCYB_2017_2762701 crossref_primary_10_1016_j_ins_2022_07_180 crossref_primary_10_1016_j_future_2018_07_047 crossref_primary_10_1109_ACCESS_2021_3086559 crossref_primary_10_1109_TPWRS_2022_3155750 crossref_primary_10_20965_jaciii_2016_p0497 crossref_primary_10_1016_j_asoc_2020_106382 crossref_primary_10_1016_j_swevo_2019_100605 crossref_primary_10_1007_s10586_024_04773_0 crossref_primary_10_1016_j_swevo_2017_05_005 crossref_primary_10_1016_j_swevo_2019_05_007 crossref_primary_10_1016_j_swevo_2019_100568 crossref_primary_10_1007_s10489_023_04717_y crossref_primary_10_1016_j_asoc_2017_03_041 crossref_primary_10_1016_j_knosys_2021_107731 crossref_primary_10_1007_s13042_018_00919_w crossref_primary_10_1007_s00500_016_2196_9 crossref_primary_10_1109_ACCESS_2019_2917899 crossref_primary_10_1016_j_asoc_2023_110095 crossref_primary_10_1109_TEVC_2017_2744674 crossref_primary_10_1016_j_engappai_2023_105889 crossref_primary_10_1109_TNNLS_2018_2806481 crossref_primary_10_1007_s00500_019_04565_4 crossref_primary_10_1016_j_applthermaleng_2025_126108 crossref_primary_10_1109_ACCESS_2020_2974324 crossref_primary_10_1016_j_asoc_2017_11_029 crossref_primary_10_1007_s00500_022_07492_z crossref_primary_10_1016_j_asoc_2023_110525 crossref_primary_10_1007_s11704_016_5552_0 crossref_primary_10_1109_TSMC_2018_2876335 crossref_primary_10_1109_TCYB_2018_2834363 crossref_primary_10_1109_TCYB_2022_3165557 crossref_primary_10_1109_TCYB_2017_2739185 crossref_primary_10_1016_j_trb_2025_103238 crossref_primary_10_1109_TEVC_2017_2671462 crossref_primary_10_1016_j_swevo_2021_100847 crossref_primary_10_1016_j_ins_2020_03_104 crossref_primary_10_1007_s40815_021_01220_9 crossref_primary_10_1109_JIOT_2020_3010834 crossref_primary_10_1109_ACCESS_2018_2832181 crossref_primary_10_1109_TSMC_2019_2898456 crossref_primary_10_1007_s42835_020_00386_w crossref_primary_10_1016_j_ins_2019_05_083 crossref_primary_10_1109_TEVC_2016_2592479 crossref_primary_10_1016_j_asoc_2019_02_026 crossref_primary_10_1016_j_swevo_2020_100794 crossref_primary_10_1080_09544828_2024_2355762 crossref_primary_10_1109_ACCESS_2020_3047699 crossref_primary_10_1016_j_swevo_2020_100790 crossref_primary_10_1155_2019_3251349 crossref_primary_10_1007_s10489_018_1183_5 crossref_primary_10_1016_j_swevo_2018_12_007 crossref_primary_10_1016_j_swevo_2023_101462 crossref_primary_10_1007_s10586_017_0895_3 crossref_primary_10_1109_TCYB_2016_2638902
Cites_doi	10.1145/1527125.1527138 10.1109/CEC.2003.1299427 10.1109/TEVC.2013.2281533 10.1145/1830483.1830577 10.1109/TEVC.2007.892759 10.1007/978-3-642-44973-4_8 10.1016/j.ejor.2006.08.008 10.1109/TEVC.2014.2339823 10.1109/CEC.2010.5586185 10.1109/TEVC.2009.2033671 10.1007/978-3-642-01020-0_35 10.1007/978-3-642-19893-9_6 10.1109/4235.996017 10.1109/SMC.2013.110 10.1016/j.swevo.2011.03.001 10.1023/A:1008202821328 10.1162/EVCO_a_00109 10.1109/TEVC.2008.925798 10.1162/106365600568202 10.1162/106365602760234108 10.1007/978-3-642-37140-0_33 10.1007/3-540-44719-9_6 10.1109/TEVC.2013.2293776 10.1109/CIS.2010.37 10.1109/TEVC.2013.2258025
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 RIA RIE AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D F28 FR3
DOI	10.1109/TEVC.2015.2457616
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 ANTE: Abstracts in New Technology & Engineering Engineering Research Database
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 Engineering Research Database ANTE: Abstracts in New Technology & Engineering
DatabaseTitleList	Technology Research Database Technology Research Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE Xplorer url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Statistics Computer Science
EISSN	1941-0026
EndPage	480
ExternalDocumentID	4073927951 10_1109_TEVC_2015_2457616 7160727
Genre	orig-research
GrantInformation_xml	– fundername: National Natural Science Foundation of China grantid: 61473241; 61273313; 61175063 funderid: 10.13039/501100001809 – fundername: Cheung Kong Scholars Programme of China grantid: K5051302050
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 F28 FR3
ID	FETCH-LOGICAL-c392t-6ecf6bb88e9ae4117d1ca21e9dce9485500c66a71df2e0d0d73a8e99dcf5e3f53
IEDL.DBID	RIE
ISICitedReferencesCount	120
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000377620600012&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	Sun Sep 28 06:47:05 EDT 2025 Sun Sep 07 03:38:39 EDT 2025 Sat Nov 29 03:13:47 EST 2025 Tue Nov 18 22:22:13 EST 2025 Tue Aug 26 16:42:57 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	3
Keywords	Constraint evolutionary multiobjective optimization decomposition approach
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c392t-6ecf6bb88e9ae4117d1ca21e9dce9485500c66a71df2e0d0d73a8e99dcf5e3f53
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
PQID	1792797801
PQPubID	85418
PageCount	6
ParticipantIDs	ieee_primary_7160727 proquest_journals_1792797801 proquest_miscellaneous_1825490457 crossref_citationtrail_10_1109_TEVC_2015_2457616 crossref_primary_10_1109_TEVC_2015_2457616
PublicationCentury	2000
PublicationDate	2016-June 2016-6-00 20160601
PublicationDateYYYYMMDD	2016-06-01
PublicationDate_xml	– month: 06 year: 2016 text: 2016-June
PublicationDecade	2010
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE transactions on evolutionary computation
PublicationTitleAbbrev	TEVC
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 ref15 ref14 zitzler (ref4) 2001 li (ref22) 2014; 18 ref30 ref11 ref10 ref2 ref17 ref16 ref19 knowles (ref32) 2006; 214 deb (ref31) 2001; 16 ref24 zitzler (ref5) 2004 ref26 ref25 ref20 ref21 jiang (ref18) 2011; 3 miettinen (ref1) 1999; 12 ref28 ref27 ref29 ref8 ref7 ref9 ref3 ref6 giagkiozis (ref23) 2012
References_xml	– ident: ref19 doi: 10.1145/1527125.1527138 – ident: ref9 doi: 10.1109/CEC.2003.1299427 – year: 2001 ident: ref4 article-title: SPEA2: Improving the strength Pareto evolutionary algorithm – ident: ref21 doi: 10.1109/TEVC.2013.2281533 – ident: ref14 doi: 10.1145/1830483.1830577 – ident: ref8 doi: 10.1109/TEVC.2007.892759 – ident: ref15 doi: 10.1007/978-3-642-44973-4_8 – volume: 16 year: 2001 ident: ref31 publication-title: Multi-Objective Optimization Using Evolutionary Algorithms – ident: ref6 doi: 10.1016/j.ejor.2006.08.008 – ident: ref20 doi: 10.1109/TEVC.2014.2339823 – ident: ref25 doi: 10.1109/CEC.2010.5586185 – ident: ref24 doi: 10.1109/TEVC.2009.2033671 – ident: ref13 doi: 10.1007/978-3-642-01020-0_35 – ident: ref27 doi: 10.1007/978-3-642-19893-9_6 – ident: ref3 doi: 10.1109/4235.996017 – ident: ref12 doi: 10.1109/SMC.2013.110 – ident: ref2 doi: 10.1016/j.swevo.2011.03.001 – volume: 3 start-page: 1260 year: 2011 ident: ref18 article-title: Multiobjective optimization by decomposition with Pareto-adaptive weight vectors publication-title: Proc 7th IEEE Int Conf Nat Comput (ICNC) – volume: 12 year: 1999 ident: ref1 publication-title: Nonlinear Multiobjective Optimization – ident: ref30 doi: 10.1023/A:1008202821328 – volume: 214 start-page: 327 year: 2006 ident: ref32 article-title: A tutorial on the performance assessment of stochastic multiobjective optimizers publication-title: TIK Report – ident: ref16 doi: 10.1162/EVCO_a_00109 – ident: ref10 doi: 10.1109/TEVC.2008.925798 – ident: ref29 doi: 10.1162/106365600568202 – ident: ref26 doi: 10.1162/106365602760234108 – ident: ref11 doi: 10.1007/978-3-642-37140-0_33 – start-page: 832 year: 2004 ident: ref5 article-title: Indicator-based selection in multiobjective search publication-title: Parallel Problem Solving from Nature-PPSN VII – year: 2012 ident: ref23 article-title: Generalized decomposition and cross entropy methods for many-objective optimization – ident: ref7 doi: 10.1007/3-540-44719-9_6 – volume: 18 start-page: 909 year: 2014 ident: ref22 article-title: Stable matching-based selection in evolutionary multiobjective optimization publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2013.2293776 – ident: ref17 doi: 10.1109/CIS.2010.37 – ident: ref28 doi: 10.1109/TEVC.2013.2258025
SSID	ssj0014519
Score	2.5196688
Snippet	A decomposition approach decomposes a multiobjective optimization problem into a number of scalar objective optimization subproblems. It plays a key role in...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	475
SubjectTerms	Algorithms constraint Constraints Convergence De-composition approach Decomposition Evolutionary algorithms Evolutionary computation Evolutionary multiobjective optimization Linear programming Measurement Optimization Scalars Sociology Statistics Strategy
Title	Constrained Subproblems in a Decomposition-Based Multiobjective Evolutionary Algorithm
URI	https://ieeexplore.ieee.org/document/7160727 https://www.proquest.com/docview/1792797801 https://www.proquest.com/docview/1825490457
Volume	20
WOSCitedRecordID	wos000377620600012&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 Xplorer 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/eLvHCXMwlV1LS8QwEB5UPOjBx6q4vojgSaz2laY5-ljxJB5U9lbSZOoDbWXdFfz3ZtJsERTBWyHp80tmJp3J9wEcyAo117wKjMp4kBoRB6rkOrC-BTEVaa4i5cQmxPV1PhzKmxk46vbCIKIrPsNjOnS5fNPoCf0qOxHEhhaLWZgVImv3anUZA6JJaYvppY0Y86HPYEahPLkd3J9TERc_jlMbXpO0-Tcf5ERVflhi514ul__3YCuw5MNIdtrivgozWPdgeSrRwPyM7cHiN77BHixQaNkyM6_BPWl1OoUINMzaDy8t886eaqbYBVKxua_oCs6srzPMbdZtyufWRrLBhx-2avTJTl8emtHT-PF1He4uB7fnV4FXWQi0jY3GQYa6ysoyz1EqTKNImEirOEJpNDrqmDDUWaZEZKoYQxMakSjb1zZXHJOKJxswVzc1bgJLdEUrakexY69USh6XRAlYJopXWiZ9CKffvdCegpze86VwS5FQFgRVQVAVHqo-HHanvLX8G391XiNsuo4elj7sTMEt_Ax9L6whigXRL0V92O-a7dyihImqsZnYPm75bINesfX7lbdhwd4_a0vHdmBuPJrgLszrD4vlaM8N0C-gquR6
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NT9wwEB3RbaXCobRLEUtpcaWeqmbJl-P4CHQRqHTVwxbtLXLsSQFBgvYDiX-Px_FGSEWVeotkx0ry7JlxZvwewBdZoeaaV4FRGQ9SI-JAlVwH1rcgpiLNVaSc2IQYj_PpVP5ag2_dWRhEdMVnOKRLl8s3jV7Sr7IDQWxosXgBL3maxmF7WqvLGRBRSltOL23MmE99DjMK5cFkdHFMZVx8GKc2wCZx8ydeyMmq_GWLnYM52fy_R3sLb3wgyQ5b5N_BGtZ92FyJNDC_Zvuw8YRxsA_rFFy23MxbcEFqnU4jAg2zFsSLy8zZVc0U-45Ubu5ruoIj6-0Mc8d1m_K6tZJsdO8nrpo9sMObP83sanF5-x5-n4wmx6eB11kItI2OFkGGusrKMs9RKkyjSJhIqzhCaTQ68pgw1FmmRGSqGEMTGpEo29c2VxyTiifb0KubGneAJbqiPbUj2bEjlZLHJZEClonilZbJAMLVdy-0JyGn97wp3GYklAVBVRBUhYdqAF-7W-5aBo5_dd4ibLqOHpYB7K3ALfwanRfWFMWCCJiiAXzumu3qopSJqrFZ2j5uA23DXrH7_Mj78Pp08vO8OD8b__gA6_ZZsraQbA96i9kSP8IrfW9xnX1yk_URps3nwQ
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=Constrained+Subproblems+in+a+Decomposition-Based+Multiobjective+Evolutionary+Algorithm&rft.jtitle=IEEE+transactions+on+evolutionary+computation&rft.au=Wang%2C+Luping&rft.au=Zhang%2C+Qingfu&rft.au=Zhou%2C+Aimin&rft.au=Gong%2C+Maoguo&rft.date=2016-06-01&rft.issn=1089-778X&rft.eissn=1941-0026&rft.volume=20&rft.issue=3&rft.spage=475&rft.epage=480&rft_id=info:doi/10.1109%2FTEVC.2015.2457616&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TEVC_2015_2457616
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