ADEMO/D: Multiobjective optimization by an adaptive differential evolution algorithm

This paper presents an approach for continuous optimization called Adaptive Differential Evolution for Multiobjective Problems (ADEMO/D). The approach incorporates concepts of Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) and mechanisms of strategies adaptation. In this work...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Neurocomputing (Amsterdam) Jg. 127; S. 65 - 77
Hauptverfasser:	Venske, Sandra M., Gonçalves, Richard A., Delgado, Myriam R.
Format:	Journal Article Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 15.03.2014 Elsevier
Schlagworte:	Adaptive algorithms Adaptive differential evolution Adaptive pursuit Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Computer science; control theory; systems Credit assignment Decision theory. Utility theory Evolutionary algorithms Exact sciences and technology Matching Mathematical programming Multiobjective optimization Operational research and scientific management Operational research. Management science Optimization Probability matching State of the art Statistical tests Strategy Theoretical computing Credit assignment Probability matching Adaptive pursuit Multiobjective optimization Adaptive differential evolution Information credibility Probabilistic approach Pareto optimum Evolutionary algorithm Reinforcement learning Multiobjective programming Conceptual analysis Adaptive method Optimization Statistical test Semantics Greedy algorithm Differential evolution Version management Mathematical programming
ISSN:	0925-2312, 1872-8286
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	This paper presents an approach for continuous optimization called Adaptive Differential Evolution for Multiobjective Problems (ADEMO/D). The approach incorporates concepts of Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) and mechanisms of strategies adaptation. In this work we test two methods to perform adaptive strategy selection: Probability Matching (PM) and Adaptive Pursuit (AP). PM and AP are analyzed in combination with four credit assignment techniques based on relative fitness improvements. The DE strategy is chosen from a candidate pool according to a probability that depends on its previous experience in generating promising solutions. In experiments, we evaluate certain features of the proposed approach, considering eight different versions while solving a well established set of 10 instances of Multiobjective Optimization Problems. Next the best-so-far version (ADEMO/D) is confronted with its non-adaptive counterparts. Finally ADEMO/D is compared with four important multiobjective optimization algorithms in the same application context. Pareto compliant indicators and statistical tests are applied to evaluate the algorithm performances. The preliminary results are very promising and stand ADEMO/D as a candidate to the state-of-the-art for multiobjective optimization. •An approach for continuous multiobjective optimization is presented in this paper.•It incorporates concepts of multiobjective evolutionary algorithms based on decomposition and mechanisms of mutation strategies adaptation.•Two methods for adaptive strategy selection are analyzed in combination with four credit assignment techniques based on relative fitness improvements.•Different versions of the proposed approach are compared and the best-so-far version is confronted with methods of state-of-the-art.•The effectiveness of the proposed approach is demonstrated on 10 instances of a multiobjective optimization benchmark.
AbstractList	This paper presents an approach for continuous optimization called Adaptive Differential Evolution for Multiobjective Problems (ADEMO/D). The approach incorporates concepts of Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) and mechanisms of strategies adaptation. In this work we test two methods to perform adaptive strategy selection: Probability Matching (PM) and Adaptive Pursuit (AP). PM and AP are analyzed in combination with four credit assignment techniques based on relative fitness improvements. The DE strategy is chosen from a candidate pool according to a probability that depends on its previous experience in generating promising solutions. In experiments, we evaluate certain features of the proposed approach, considering eight different versions while solving a well established set of 10 instances of Multiobjective Optimization Problems. Next the best-so-far version (ADEMO/D) is confronted with its non-adaptive counterparts. Finally ADEMO/D is compared with four important multiobjective optimization algorithms in the same application context. Pareto compliant indicators and statistical tests are applied to evaluate the algorithm performances. The preliminary results are very promising and stand ADEMO/D as a candidate to the state-of-the-art for multiobjective optimization. •An approach for continuous multiobjective optimization is presented in this paper.•It incorporates concepts of multiobjective evolutionary algorithms based on decomposition and mechanisms of mutation strategies adaptation.•Two methods for adaptive strategy selection are analyzed in combination with four credit assignment techniques based on relative fitness improvements.•Different versions of the proposed approach are compared and the best-so-far version is confronted with methods of state-of-the-art.•The effectiveness of the proposed approach is demonstrated on 10 instances of a multiobjective optimization benchmark. This paper presents an approach for continuous optimization called Adaptive Differential Evolution for Multiobjective Problems (ADEMO/D). The approach incorporates concepts of Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) and mechanisms of strategies adaptation. In this work we test two methods to perform adaptive strategy selection: Probability Matching (PM) and Adaptive Pursuit (AP). PM and AP are analyzed in combination with four credit assignment techniques based on relative fitness improvements. The DE strategy is chosen from a candidate pool according to a probability that depends on its previous experience in generating promising solutions. In experiments, we evaluate certain features of the proposed approach, considering eight different versions while solving a well established set of 10 instances of Multiobjective Optimization Problems. Next the best-so-far version (ADEMO/D) is confronted with its non-adaptive counterparts. Finally ADEMO/D is compared with four important multiobjective optimization algorithms in the same application context. Pareto compliant indicators and statistical tests are applied to evaluate the algorithm performances. The preliminary results are very promising and stand ADEMO/D as a candidate to the state-of-the-art for multiobjective optimization.
Author	Venske, Sandra M. Delgado, Myriam R. Gonçalves, Richard A.
Author_xml	– sequence: 1 givenname: Sandra M. surname: Venske fullname: Venske, Sandra M. email: sandravenske@gmail.com organization: Department of Computer Science, UNICENTRO, Guarapuava, Brazil – sequence: 2 givenname: Richard A. surname: Gonçalves fullname: Gonçalves, Richard A. organization: Department of Computer Science, UNICENTRO, Guarapuava, Brazil – sequence: 3 givenname: Myriam R. surname: Delgado fullname: Delgado, Myriam R. organization: CPGEI/DAINF, UTFPR, Curitiba, Brazil
BackLink	http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28284313$$DView record in Pascal Francis
BookMark	eNqFkctqHDEQRUVwIGMnf5BFbwLZdFuvfnkRMLbzABtvnLWoVpcSDWppIqkH7K-PPONsskhWBapzL9TRKTnxwSMh7xltGGXd-bbxuOqwNJwy0dCuoVK8Ihs29Lwe-NCdkA0deVtzwfgbcprSllLWMz5uyMPl9c3d_fn1RXW3umzDtEWd7R6rsMt2sU9Q3nw1PVbgK5hhd9jN1hiM6LMFV-E-uPVAgfsRos0_l7fktQGX8N3LPCPfP988XH2tb--_fLu6vK216MZcswl6I3uqp9m0RnZtC2A6KbHM2Qyin0Yj22FoR6N7LifKe8rnQQoB0yRnKs7Ix2PvLoZfK6asFps0Ogcew5oU60dRzm_pWNAPLygkDc5E8NomtYt2gfioeNEkBROFuzhyOoaUIhqlbT5IyBGsU4yqZ-Vqq47K1bNyRTtVlJew_Cv8p_8_sU_HGBZXe4tRJW3Ra5xtLL-h5mD_XfAbJb2gBw
CitedBy_id	crossref_primary_10_1016_j_neucom_2020_01_114 crossref_primary_10_1109_ACCESS_2022_3188762 crossref_primary_10_1016_j_eswa_2015_01_061 crossref_primary_10_1016_j_neucom_2019_10_122 crossref_primary_10_1109_TCYB_2018_2834363 crossref_primary_10_1109_TEVC_2022_3140265 crossref_primary_10_1007_s40747_022_00812_8 crossref_primary_10_1177_1077546317743890 crossref_primary_10_1016_j_cor_2016_08_012 crossref_primary_10_1109_TCYB_2017_2739185 crossref_primary_10_1016_j_swevo_2018_02_008 crossref_primary_10_1016_j_eswa_2016_03_009 crossref_primary_10_1080_0305215X_2017_1337756 crossref_primary_10_1007_s00500_016_2331_7 crossref_primary_10_1016_j_ins_2015_12_022 crossref_primary_10_1016_j_ins_2017_11_030 crossref_primary_10_1016_j_engappai_2018_11_002 crossref_primary_10_1016_j_neucom_2015_09_111 crossref_primary_10_1016_j_swevo_2016_01_004 crossref_primary_10_1109_TEVC_2016_2608507 crossref_primary_10_1155_2021_6614283 crossref_primary_10_1080_09540091_2021_1984396 crossref_primary_10_1109_TAI_2025_3545792 crossref_primary_10_1007_s00779_019_01211_6 crossref_primary_10_1007_s10586_025_05203_5 crossref_primary_10_1007_s00500_020_05457_8 crossref_primary_10_1016_j_physa_2019_123526 crossref_primary_10_1016_j_neucom_2025_130012 crossref_primary_10_1016_j_neucom_2014_08_097 crossref_primary_10_1109_TEVC_2022_3168427 crossref_primary_10_1155_2017_6439631 crossref_primary_10_1364_AO_382215 crossref_primary_10_1109_ACCESS_2019_2909290 crossref_primary_10_1007_s00521_019_04608_9 crossref_primary_10_1007_s00500_020_05347_z crossref_primary_10_1007_s12293_021_00330_z crossref_primary_10_1016_j_cor_2015_04_003 crossref_primary_10_1007_s12293_019_00290_5 crossref_primary_10_1016_j_swevo_2020_100670 crossref_primary_10_1016_j_compag_2017_05_034 crossref_primary_10_1016_j_neucom_2018_12_012 crossref_primary_10_1016_j_swevo_2020_100790 crossref_primary_10_1016_j_neucom_2014_02_058 crossref_primary_10_1016_j_neucom_2015_08_092 crossref_primary_10_1016_j_asoc_2017_05_033 crossref_primary_10_1007_s13748_018_0155_7 crossref_primary_10_1080_00207543_2023_2254405 crossref_primary_10_3233_IFS_151752
Cites_doi	10.1109/ICNC.2010.5583335 10.1109/TEVC.2003.819944 10.1109/TEVC.2008.925798 10.1109/TEVC.2008.927706 10.1145/1068009.1068251 10.1007/BF00116878 10.1109/TEVC.2011.2166159 10.1007/978-3-642-20407-4_19 10.1016/j.neucom.2012.02.017 10.1016/j.ins.2011.07.049 10.1016/j.asoc.2010.04.024 10.1109/TEVC.2006.872133 10.1007/s10479-006-0074-z 10.1109/4235.996017 10.1023/A:1008202821328 10.1109/SBRN.2012.29 10.1109/TEVC.2008.2009457 10.1016/j.asoc.2012.03.067 10.1016/j.neucom.2011.02.006 10.1016/j.asoc.2006.12.005 10.1109/TEVC.2010.2083670 10.1109/TEVC.2009.2033671 10.1109/CEC.2009.4982949 10.1109/TEVC.2009.2014613 10.1007/s00500-005-0537-1 10.1016/j.asoc.2011.05.002 10.1109/TEVC.2010.2081369 10.1145/1830483.1830559 10.1109/TEVC.2007.892759 10.1016/j.asoc.2010.05.007 10.1007/978-3-642-25566-3_37 10.1007/s00500-011-0704-5 10.1145/1569901.1570009
ContentType	Journal Article Conference Proceeding
Copyright	2013 Elsevier B.V. 2015 INIST-CNRS
Copyright_xml	– notice: 2013 Elsevier B.V. – notice: 2015 INIST-CNRS
DBID	AAYXX CITATION IQODW 7SC 8FD JQ2 L7M L~C L~D
DOI	10.1016/j.neucom.2013.06.043
DatabaseName	CrossRef Pascal-Francis Computer and Information Systems 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 Computer and Information Systems Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Advanced Technologies Database with Aerospace ProQuest Computer Science Collection Computer and Information Systems Abstracts Professional
DatabaseTitleList	Computer and Information Systems Abstracts
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science Applied Sciences
EISSN	1872-8286
EndPage	77
ExternalDocumentID	28284313 10_1016_j_neucom_2013_06_043 S0925231213007765
GroupedDBID	--- --K --M .DC .~1 0R~ 123 1B1 1~. 1~5 4.4 457 4G. 53G 5VS 7-5 71M 8P~ 9JM 9JN AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXLA AAXUO AAYFN ABBOA ABCQJ ABFNM ABJNI ABMAC ABXDB ACDAQ ACGFS ACRLP ACZNC ADBBV ADEZE AEBSH AEKER AENEX AFKWA AFTJW AFXIZ AGHFR AGUBO AGWIK AGYEJ AHHHB AHZHX AIALX AIEXJ AIKHN AITUG AJOXV AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD AXJTR BKOJK BLXMC CS3 DU5 EBS EFJIC EJD EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ IHE J1W KOM LG9 M41 MO0 MOBAO N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RIG ROL RPZ SDF SDG SDP SES SPC SPCBC SSN SSV SSZ T5K ZMT ~G- 29N 9DU AAQXK AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EFLBG FEDTE FGOYB HLZ HVGLF HZ~ R2- SBC SEW WUQ XPP ~HD AGCQF AGRNS BNPGV IQODW SSH 7SC 8FD JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c369t-1ba7f470cbdf5f4655aaf644e5aadf837b9f458859fc724b02702d8433abb4d03
ISICitedReferencesCount	62
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000329603100008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0925-2312
IngestDate	Sun Sep 28 10:11:38 EDT 2025 Mon Jul 21 09:14:25 EDT 2025 Sat Nov 29 04:38:52 EST 2025 Tue Nov 18 22:40:45 EST 2025 Tue Jun 18 08:52:14 EDT 2024
IsPeerReviewed	true
IsScholarly	true
Keywords	Credit assignment Probability matching Adaptive pursuit Multiobjective optimization Adaptive differential evolution Information credibility Probabilistic approach Pareto optimum Evolutionary algorithm Reinforcement learning Multiobjective programming Conceptual analysis Adaptive method Optimization Statistical test Semantics Greedy algorithm Differential evolution Version management Mathematical programming
Language	English
License	CC BY 4.0
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-c369t-1ba7f470cbdf5f4655aaf644e5aadf837b9f458859fc724b02702d8433abb4d03
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
PQID	1793286509
PQPubID	23500
PageCount	13
ParticipantIDs	proquest_miscellaneous_1793286509 pascalfrancis_primary_28284313 crossref_citationtrail_10_1016_j_neucom_2013_06_043 crossref_primary_10_1016_j_neucom_2013_06_043 elsevier_sciencedirect_doi_10_1016_j_neucom_2013_06_043
PublicationCentury	2000
PublicationDate	2014-03-15
PublicationDateYYYYMMDD	2014-03-15
PublicationDate_xml	– month: 03 year: 2014 text: 2014-03-15 day: 15
PublicationDecade	2010
PublicationPlace	Amsterdam
PublicationPlace_xml	– name: Amsterdam
PublicationTitle	Neurocomputing (Amsterdam)
PublicationYear	2014
Publisher	Elsevier B.V Elsevier
Publisher_xml	– name: Elsevier B.V – name: Elsevier
References	H. Abbass, The self-adaptive Pareto differential evolution algorithm, in: IEEE Congress on Evolutionary Computation, 2002, pp. 831–836. Zhang, Li (bib24) 2007; 11 S. Venske, R.A. Gonçalves, M. Delgado, ADEMO/D: adaptive differential evolution for multiobjective problems, in: Brazilian Conference on Neural Networks 2012, 2012, pp. 226–231. J. Zhang, A. Sanderson, JADE: self-adaptive differential evolution with fast and reliable convergence performance, in: IEEE Congress on Evolutionary Computation, 2007, pp. 2251–2258. W. Gong, A. Fialho, Z. Cai, Adaptive strategy selection in differential evolution, in: Conference on Genetic and Evolutionary Computation, 2010, pp. 409–416. Liao (bib31) 2010; 10 Ehrgott (bib39) 2006; 147 Sindhya, Ruuska, Haanp, Miettinen (bib30) 2011; 15 D. Thierens, An adaptive pursuit strategy for allocating operator probabilities, in: Conference on Genetic and Evolutionary Computation, 2005, pp. 1539–1546. Teo (bib17) 2006; 10 Mallipeddi, Suganthan, Pan, Tasgetiren (bib23) 2011; 11 Zhang, Sanderson (bib6) 2009; 13 Coello Coello, Lamont, Van Veldhuizen (bib4) 2007 Q. Zhang, A. Zhou, S. Zhao, P. N. Suganthan, W. Liu, S. Tiwari, Multiobjective optimization test instances for the CEC 2009 special session and competition, Technical Report, University of Essex and Nanyang Technological University, CES-487, 2008. Brest, Greiner, Boskovic, Mernik, Zumer (bib15) 2006; 10 Lu, Chang, Tsai (bib32) 2012; 89 Engelbrecht (bib11) 2007 Ong, Keane (bib40) 2004; 8 C.A.C. Coello, An updated survey of evolutionary multiobjective optimization techniques: state of the art and future trends, in: IEEE Congress on Evolutionary Computation, 1999, pp. 3–13. Price, Storn, Lampinen (bib5) 2005 Zhang, Liu, Tsang, Virginas (bib26) 2010; 14 Subudhi, Jena (bib34) 2011; 74 Zhao, Suganthan, Zhang (bib37) 2012; 16 A. Fialho, M. Schoenauer, M. Sebag, Analysis of adaptive operator selection techniques on the royal road and long k-path problems, in: Conference on Genetic and Evolutionary Computation, 2009, pp. 779–786. Dorronsoro, Bouvry (bib8) 2011; 15 Nobakhti, Wang (bib16) 2008; 8 Kalyanmoy (bib46) 2004 Q. Zhang, W. Liu, H. Li, The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances, in: Congress on Evolutionary Computation, 2009, pp. 203–208. Qasem, Shamsuddin (bib33) 2011; 11 Goldberg (bib36) 1990; 5 Goldberg (bib27) 1990; 5 Storn, Price (bib3) 1997; 11 Epitropakis, Tasoulis, Pavlidis, Plagianakos, Vrahatis (bib9) 2011; 15 R. Gamperle, S.D. Muller, P. Koumoutsakos, A parameter study for differential evolution, in: WSEAS International Conference on Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, 2002, pp. 293–298. K.T. Fang, C. Ma, Orthogonal and Uniform Design, Science Press, 2001 (in Chinese). Gong, Fialho, Cai, Li (bib22) 2011; 181 M.H. Kim, R.I. McKay, N.X. Hoai, K. Kim, Operator self-adaptation in genetic programming, in: Proceedings of the 14th European Conference on Genetic programming, EuroGP’11, Springer-Verlag, Berlin, Heidelberg, 2011, pp. 215–226. Deb (bib1) 2001 A.K. Qin, P.N. Suganthan, Self-adaptive differential evolution algorithm for numerical optimization, in: IEEE Congress on Evolutionary Computation, 2005, pp. 1785–1791. J. Brest, V. Zumer, M.S. Maucec, Self-adaptive differential evolution algorithm in constrained real-parameter optimization, in: IEEE Congress on Evolutionary Computation, 2006, pp. 215–222. K. Li, A. Fialho, S. Kwong, Multi-objective differential evolution with adaptive control of parameters and operators, in: International Conference on Learning and Intelligent Optimization, 2011, pp. 473–487. Khan Mashwani, Salhi (bib47) 2012; 12 W. Huang, H. Li, On the differential evolution schemes in MOEA/D, in: International Conference on Natural Computation, 2010, pp. 2788–2792. Bleuler, Laumanns, Thiele, Zitzler (bib43) 2003 Deb, Pratap, Agarwal, Meyarivan (bib45) 2002; 6 Qin, Huang, Suganthan (bib19) 2009; 13 Li, Zhang (bib25) 2009; 13 Das, Abraham, Chakraborty, Konar (bib7) 2009; 13 Deb (10.1016/j.neucom.2013.06.043_bib1) 2001 10.1016/j.neucom.2013.06.043_bib41 Zhang (10.1016/j.neucom.2013.06.043_bib6) 2009; 13 10.1016/j.neucom.2013.06.043_bib44 Brest (10.1016/j.neucom.2013.06.043_bib15) 2006; 10 10.1016/j.neucom.2013.06.043_bib42 10.1016/j.neucom.2013.06.043_bib48 10.1016/j.neucom.2013.06.043_bib49 Gong (10.1016/j.neucom.2013.06.043_bib22) 2011; 181 10.1016/j.neucom.2013.06.043_bib2 Epitropakis (10.1016/j.neucom.2013.06.043_bib9) 2011; 15 Goldberg (10.1016/j.neucom.2013.06.043_bib27) 1990; 5 Das (10.1016/j.neucom.2013.06.043_bib7) 2009; 13 Storn (10.1016/j.neucom.2013.06.043_bib3) 1997; 11 Price (10.1016/j.neucom.2013.06.043_bib5) 2005 Sindhya (10.1016/j.neucom.2013.06.043_bib30) 2011; 15 Ong (10.1016/j.neucom.2013.06.043_bib40) 2004; 8 Khan Mashwani (10.1016/j.neucom.2013.06.043_bib47) 2012; 12 Zhang (10.1016/j.neucom.2013.06.043_bib26) 2010; 14 10.1016/j.neucom.2013.06.043_bib38 10.1016/j.neucom.2013.06.043_bib35 Deb (10.1016/j.neucom.2013.06.043_bib45) 2002; 6 Subudhi (10.1016/j.neucom.2013.06.043_bib34) 2011; 74 Mallipeddi (10.1016/j.neucom.2013.06.043_bib23) 2011; 11 Nobakhti (10.1016/j.neucom.2013.06.043_bib16) 2008; 8 Qin (10.1016/j.neucom.2013.06.043_bib19) 2009; 13 Bleuler (10.1016/j.neucom.2013.06.043_bib43) 2003 10.1016/j.neucom.2013.06.043_bib20 10.1016/j.neucom.2013.06.043_bib21 10.1016/j.neucom.2013.06.043_bib28 Lu (10.1016/j.neucom.2013.06.043_bib32) 2012; 89 10.1016/j.neucom.2013.06.043_bib29 Dorronsoro (10.1016/j.neucom.2013.06.043_bib8) 2011; 15 Ehrgott (10.1016/j.neucom.2013.06.043_bib39) 2006; 147 Liao (10.1016/j.neucom.2013.06.043_bib31) 2010; 10 Kalyanmoy (10.1016/j.neucom.2013.06.043_bib46) 2004 10.1016/j.neucom.2013.06.043_bib12 Zhang (10.1016/j.neucom.2013.06.043_bib24) 2007; 11 10.1016/j.neucom.2013.06.043_bib10 10.1016/j.neucom.2013.06.043_bib13 Goldberg (10.1016/j.neucom.2013.06.043_bib36) 1990; 5 Engelbrecht (10.1016/j.neucom.2013.06.043_bib11) 2007 10.1016/j.neucom.2013.06.043_bib14 10.1016/j.neucom.2013.06.043_bib18 Zhao (10.1016/j.neucom.2013.06.043_bib37) 2012; 16 Teo (10.1016/j.neucom.2013.06.043_bib17) 2006; 10 Qasem (10.1016/j.neucom.2013.06.043_bib33) 2011; 11 Coello Coello (10.1016/j.neucom.2013.06.043_bib4) 2007 Li (10.1016/j.neucom.2013.06.043_bib25) 2009; 13
References_xml	– volume: 15 start-page: 99 year: 2011 end-page: 119 ident: bib9 article-title: Enhancing differential evolution utilizing proximity-based mutation operators publication-title: IEEE Transactions on Evolutionary Computation – start-page: 494 year: 2003 end-page: 508 ident: bib43 article-title: PISA – a platform and programming language independent interface for search algorithms publication-title: Evolutionary Multi-Criterion Optimization – reference: J. Brest, V. Zumer, M.S. Maucec, Self-adaptive differential evolution algorithm in constrained real-parameter optimization, in: IEEE Congress on Evolutionary Computation, 2006, pp. 215–222. – reference: W. Gong, A. Fialho, Z. Cai, Adaptive strategy selection in differential evolution, in: Conference on Genetic and Evolutionary Computation, 2010, pp. 409–416. – volume: 14 start-page: 456 year: 2010 end-page: 474 ident: bib26 article-title: Expensive multiobjective optimization by MOEA/D with Gaussian process model publication-title: IEEE Transactions on Evolutionary Computation – reference: S. Venske, R.A. Gonçalves, M. Delgado, ADEMO/D: adaptive differential evolution for multiobjective problems, in: Brazilian Conference on Neural Networks 2012, 2012, pp. 226–231. – volume: 11 start-page: 712 year: 2007 end-page: 731 ident: bib24 article-title: MOEA/D publication-title: IEEE Transactions on Evolutionary Computation – volume: 12 start-page: 2765 year: 2012 end-page: 2780 ident: bib47 article-title: A decomposition-based hybrid multiobjective evolutionary algorithm with dynamic resource allocation publication-title: Applied Soft Computing – volume: 15 start-page: 67 year: 2011 end-page: 98 ident: bib8 article-title: Improving classical and decentralized differential evolution with new mutation operator and population topologies publication-title: IEEE Transactions on Evolutionary Computation – volume: 11 start-page: 5565 year: 2011 end-page: 5581 ident: bib33 article-title: Memetic elitist Pareto differential evolution algorithm based radial basis function networks for classification problems publication-title: Applied Soft Computing – reference: Q. Zhang, A. Zhou, S. Zhao, P. N. Suganthan, W. Liu, S. Tiwari, Multiobjective optimization test instances for the CEC 2009 special session and competition, Technical Report, University of Essex and Nanyang Technological University, CES-487, 2008. – reference: A. Fialho, M. Schoenauer, M. Sebag, Analysis of adaptive operator selection techniques on the royal road and long k-path problems, in: Conference on Genetic and Evolutionary Computation, 2009, pp. 779–786. – reference: H. Abbass, The self-adaptive Pareto differential evolution algorithm, in: IEEE Congress on Evolutionary Computation, 2002, pp. 831–836. – reference: C.A.C. Coello, An updated survey of evolutionary multiobjective optimization techniques: state of the art and future trends, in: IEEE Congress on Evolutionary Computation, 1999, pp. 3–13. – volume: 10 start-page: 673 year: 2006 end-page: 686 ident: bib17 article-title: Exploring dynamic self-adaptive populations in differential evolution publication-title: Soft Computing – volume: 10 start-page: 1188 year: 2010 end-page: 1199 ident: bib31 article-title: Two hybrid differential evolution algorithms for engineering design optimization publication-title: Applied Soft Computing – year: 2007 ident: bib4 article-title: Evolutionary Algorithms for Solving Multi-Objective Problems – reference: J. Zhang, A. Sanderson, JADE: self-adaptive differential evolution with fast and reliable convergence performance, in: IEEE Congress on Evolutionary Computation, 2007, pp. 2251–2258. – volume: 13 start-page: 945 year: 2009 end-page: 958 ident: bib6 article-title: JADE publication-title: IEEE Transactions on Evolutionary Computation – reference: K. Li, A. Fialho, S. Kwong, Multi-objective differential evolution with adaptive control of parameters and operators, in: International Conference on Learning and Intelligent Optimization, 2011, pp. 473–487. – reference: A.K. Qin, P.N. Suganthan, Self-adaptive differential evolution algorithm for numerical optimization, in: IEEE Congress on Evolutionary Computation, 2005, pp. 1785–1791. – volume: 74 start-page: 1696 year: 2011 end-page: 1709 ident: bib34 article-title: Nonlinear system identification using memetic differential evolution trained neural networks publication-title: Neurocomputing – volume: 89 start-page: 178 year: 2012 end-page: 192 ident: bib32 article-title: Parameter estimation of fuzzy neural network controller based on a modified differential evolution publication-title: Neurocomputing – reference: M.H. Kim, R.I. McKay, N.X. Hoai, K. Kim, Operator self-adaptation in genetic programming, in: Proceedings of the 14th European Conference on Genetic programming, EuroGP’11, Springer-Verlag, Berlin, Heidelberg, 2011, pp. 215–226. – year: 2004 ident: bib46 article-title: Multi-Objective Optimization using Evolutionary Algorithms – volume: 11 start-page: 341 year: 1997 end-page: 359 ident: bib3 article-title: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces publication-title: Journal of Global Optimization – volume: 13 start-page: 526 year: 2009 end-page: 553 ident: bib7 article-title: Differential evolution using a neighborhood-based mutation operator publication-title: IEEE Transactions on Evolutionary Computation – volume: 13 start-page: 398 year: 2009 end-page: 417 ident: bib19 article-title: Differential evolution algorithm with strategy adaptation for global numerical optimization publication-title: IEEE Transactions on Evolutionary Computation – volume: 8 start-page: 350 year: 2008 end-page: 370 ident: bib16 article-title: A simple self-adaptive differential evolution algorithm with application on the ALSTOM gasifier publication-title: Applied Soft Computing – volume: 10 start-page: 646 year: 2006 end-page: 657 ident: bib15 article-title: Self-adapting control parameters in differential evolution publication-title: IEEE Transactions on Evolutionary Computation – volume: 16 start-page: 442 year: 2012 end-page: 446 ident: bib37 article-title: Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes publication-title: IEEE Transactions on Evolutionary Computation – volume: 6 start-page: 182 year: 2002 end-page: 197 ident: bib45 article-title: A fast and elitist multiobjective genetic algorithm publication-title: IEEE Transactions on Evolutionary Computation – year: 2007 ident: bib11 article-title: Computational Intelligence – reference: K.T. Fang, C. Ma, Orthogonal and Uniform Design, Science Press, 2001 (in Chinese). – year: 2005 ident: bib5 article-title: Differential Evolution – reference: W. Huang, H. Li, On the differential evolution schemes in MOEA/D, in: International Conference on Natural Computation, 2010, pp. 2788–2792. – volume: 15 start-page: 2041 year: 2011 end-page: 2055 ident: bib30 article-title: A new hybrid mutation operator for multiobjective optimization with differential evolution publication-title: Soft Computing – reference: R. Gamperle, S.D. Muller, P. Koumoutsakos, A parameter study for differential evolution, in: WSEAS International Conference on Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, 2002, pp. 293–298. – volume: 5 start-page: 407 year: 1990 end-page: 425 ident: bib36 article-title: Probability matching, the magnitude of reinforcement, and classifier system bidding publication-title: Machine Learning – volume: 13 start-page: 284 year: 2009 end-page: 302 ident: bib25 article-title: Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II publication-title: IEEE Transactions on Evolutionary Computation – volume: 5 start-page: 407 year: 1990 end-page: 425 ident: bib27 article-title: Probability matching, the magnitude of reinforcement, and classifier system bidding publication-title: Machine Learning – reference: Q. Zhang, W. Liu, H. Li, The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances, in: Congress on Evolutionary Computation, 2009, pp. 203–208. – year: 2001 ident: bib1 article-title: Multi-Objective Optimization using Evolutionary Algorithms, Wiley-Interscience Series in Systems and Optimization – volume: 147 start-page: 343 year: 2006 end-page: 360 ident: bib39 article-title: A discussion of scalarization techniques for multiple objective integer programming publication-title: Annals of Operations Research – volume: 8 start-page: 99 year: 2004 end-page: 110 ident: bib40 article-title: Meta-lamarckian learning in memetic algorithms publication-title: IEEE Transactions on Evolutionary Computation – volume: 181 start-page: 5364 year: 2011 end-page: 5386 ident: bib22 article-title: Adaptive strategy selection in differential evolution for numerical optimization publication-title: Information Sciences – volume: 11 start-page: 1679 year: 2011 end-page: 1696 ident: bib23 article-title: Differential evolution algorithm with ensemble of parameters and mutation strategies publication-title: Applied Soft Computing – reference: D. Thierens, An adaptive pursuit strategy for allocating operator probabilities, in: Conference on Genetic and Evolutionary Computation, 2005, pp. 1539–1546. – ident: 10.1016/j.neucom.2013.06.043_bib38 doi: 10.1109/ICNC.2010.5583335 – volume: 8 start-page: 99 issue: 2 year: 2004 ident: 10.1016/j.neucom.2013.06.043_bib40 article-title: Meta-lamarckian learning in memetic algorithms publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2003.819944 – volume: 13 start-page: 284 issue: 2 year: 2009 ident: 10.1016/j.neucom.2013.06.043_bib25 article-title: Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2008.925798 – volume: 13 start-page: 398 year: 2009 ident: 10.1016/j.neucom.2013.06.043_bib19 article-title: Differential evolution algorithm with strategy adaptation for global numerical optimization publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2008.927706 – ident: 10.1016/j.neucom.2013.06.043_bib35 doi: 10.1145/1068009.1068251 – ident: 10.1016/j.neucom.2013.06.043_bib12 – volume: 5 start-page: 407 year: 1990 ident: 10.1016/j.neucom.2013.06.043_bib36 article-title: Probability matching, the magnitude of reinforcement, and classifier system bidding publication-title: Machine Learning doi: 10.1007/BF00116878 – volume: 16 start-page: 442 issue: 3 year: 2012 ident: 10.1016/j.neucom.2013.06.043_bib37 article-title: Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2011.2166159 – volume: 5 start-page: 407 issue: 4 year: 1990 ident: 10.1016/j.neucom.2013.06.043_bib27 article-title: Probability matching, the magnitude of reinforcement, and classifier system bidding publication-title: Machine Learning doi: 10.1007/BF00116878 – year: 2001 ident: 10.1016/j.neucom.2013.06.043_bib1 – ident: 10.1016/j.neucom.2013.06.043_bib44 doi: 10.1007/978-3-642-20407-4_19 – ident: 10.1016/j.neucom.2013.06.043_bib49 – volume: 89 start-page: 178 issue: 0 year: 2012 ident: 10.1016/j.neucom.2013.06.043_bib32 article-title: Parameter estimation of fuzzy neural network controller based on a modified differential evolution publication-title: Neurocomputing doi: 10.1016/j.neucom.2012.02.017 – volume: 181 start-page: 5364 issue: 24 year: 2011 ident: 10.1016/j.neucom.2013.06.043_bib22 article-title: Adaptive strategy selection in differential evolution for numerical optimization publication-title: Information Sciences doi: 10.1016/j.ins.2011.07.049 – volume: 11 start-page: 1679 issue: 2 year: 2011 ident: 10.1016/j.neucom.2013.06.043_bib23 article-title: Differential evolution algorithm with ensemble of parameters and mutation strategies publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2010.04.024 – volume: 10 start-page: 646 issue: 6 year: 2006 ident: 10.1016/j.neucom.2013.06.043_bib15 article-title: Self-adapting control parameters in differential evolution publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2006.872133 – start-page: 494 year: 2003 ident: 10.1016/j.neucom.2013.06.043_bib43 article-title: PISA – a platform and programming language independent interface for search algorithms – volume: 147 start-page: 343 year: 2006 ident: 10.1016/j.neucom.2013.06.043_bib39 article-title: A discussion of scalarization techniques for multiple objective integer programming publication-title: Annals of Operations Research doi: 10.1007/s10479-006-0074-z – volume: 6 start-page: 182 issue: 2 year: 2002 ident: 10.1016/j.neucom.2013.06.043_bib45 article-title: A fast and elitist multiobjective genetic algorithm publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.996017 – ident: 10.1016/j.neucom.2013.06.043_bib28 doi: 10.1145/1068009.1068251 – ident: 10.1016/j.neucom.2013.06.043_bib42 – year: 2007 ident: 10.1016/j.neucom.2013.06.043_bib11 – volume: 11 start-page: 341 issue: 4 year: 1997 ident: 10.1016/j.neucom.2013.06.043_bib3 article-title: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces publication-title: Journal of Global Optimization doi: 10.1023/A:1008202821328 – year: 2007 ident: 10.1016/j.neucom.2013.06.043_bib4 – ident: 10.1016/j.neucom.2013.06.043_bib10 doi: 10.1109/SBRN.2012.29 – volume: 13 start-page: 526 issue: 3 year: 2009 ident: 10.1016/j.neucom.2013.06.043_bib7 article-title: Differential evolution using a neighborhood-based mutation operator publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2008.2009457 – ident: 10.1016/j.neucom.2013.06.043_bib29 – volume: 12 start-page: 2765 issue: 9 year: 2012 ident: 10.1016/j.neucom.2013.06.043_bib47 article-title: A decomposition-based hybrid multiobjective evolutionary algorithm with dynamic resource allocation publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2012.03.067 – volume: 74 start-page: 1696 issue: 10 year: 2011 ident: 10.1016/j.neucom.2013.06.043_bib34 article-title: Nonlinear system identification using memetic differential evolution trained neural networks publication-title: Neurocomputing doi: 10.1016/j.neucom.2011.02.006 – year: 2004 ident: 10.1016/j.neucom.2013.06.043_bib46 – volume: 8 start-page: 350 issue: 1 year: 2008 ident: 10.1016/j.neucom.2013.06.043_bib16 article-title: A simple self-adaptive differential evolution algorithm with application on the ALSTOM gasifier publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2006.12.005 – year: 2005 ident: 10.1016/j.neucom.2013.06.043_bib5 – ident: 10.1016/j.neucom.2013.06.043_bib14 – ident: 10.1016/j.neucom.2013.06.043_bib18 – volume: 15 start-page: 99 issue: 1 year: 2011 ident: 10.1016/j.neucom.2013.06.043_bib9 article-title: Enhancing differential evolution utilizing proximity-based mutation operators publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2010.2083670 – volume: 14 start-page: 456 issue: 3 year: 2010 ident: 10.1016/j.neucom.2013.06.043_bib26 article-title: Expensive multiobjective optimization by MOEA/D with Gaussian process model publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2009.2033671 – ident: 10.1016/j.neucom.2013.06.043_bib48 doi: 10.1109/CEC.2009.4982949 – volume: 13 start-page: 945 year: 2009 ident: 10.1016/j.neucom.2013.06.043_bib6 article-title: JADE publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2009.2014613 – volume: 10 start-page: 673 year: 2006 ident: 10.1016/j.neucom.2013.06.043_bib17 article-title: Exploring dynamic self-adaptive populations in differential evolution publication-title: Soft Computing doi: 10.1007/s00500-005-0537-1 – ident: 10.1016/j.neucom.2013.06.043_bib2 – volume: 11 start-page: 5565 issue: 8 year: 2011 ident: 10.1016/j.neucom.2013.06.043_bib33 article-title: Memetic elitist Pareto differential evolution algorithm based radial basis function networks for classification problems publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2011.05.002 – ident: 10.1016/j.neucom.2013.06.043_bib13 – volume: 15 start-page: 67 issue: 1 year: 2011 ident: 10.1016/j.neucom.2013.06.043_bib8 article-title: Improving classical and decentralized differential evolution with new mutation operator and population topologies publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2010.2081369 – ident: 10.1016/j.neucom.2013.06.043_bib21 doi: 10.1145/1830483.1830559 – volume: 11 start-page: 712 issue: 6 year: 2007 ident: 10.1016/j.neucom.2013.06.043_bib24 article-title: MOEA/D publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2007.892759 – volume: 10 start-page: 1188 issue: 4 year: 2010 ident: 10.1016/j.neucom.2013.06.043_bib31 article-title: Two hybrid differential evolution algorithms for engineering design optimization publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2010.05.007 – ident: 10.1016/j.neucom.2013.06.043_bib20 doi: 10.1007/978-3-642-25566-3_37 – volume: 15 start-page: 2041 issue: 10 year: 2011 ident: 10.1016/j.neucom.2013.06.043_bib30 article-title: A new hybrid mutation operator for multiobjective optimization with differential evolution publication-title: Soft Computing doi: 10.1007/s00500-011-0704-5 – ident: 10.1016/j.neucom.2013.06.043_bib41 doi: 10.1145/1569901.1570009
SSID	ssj0017129
Score	2.35419
Snippet	This paper presents an approach for continuous optimization called Adaptive Differential Evolution for Multiobjective Problems (ADEMO/D). The approach...
SourceID	proquest pascalfrancis crossref elsevier
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	65
SubjectTerms	Adaptive algorithms Adaptive differential evolution Adaptive pursuit Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Computer science; control theory; systems Credit assignment Decision theory. Utility theory Evolutionary algorithms Exact sciences and technology Matching Mathematical programming Multiobjective optimization Operational research and scientific management Operational research. Management science Optimization Probability matching State of the art Statistical tests Strategy Theoretical computing
Title	ADEMO/D: Multiobjective optimization by an adaptive differential evolution algorithm
URI	https://dx.doi.org/10.1016/j.neucom.2013.06.043 https://www.proquest.com/docview/1793286509
Volume	127
WOSCitedRecordID	wos000329603100008&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-8286 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017129 issn: 0925-2312 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lj9owELbobg-VKvW1Velj5Uq9rdKGvJz0hpZ0-0Cw0lLELbIdZ7sIEgQB7R763zuO7RBKK9RDLwEZEoO_LzPjyTwQeieIHfDMY5bPO6HlidC3IkKJxb2QpbaTOlEV8j_uk8EgnEyiy1brp8mF2cxInoe3t9Hiv0INYwC2TJ39B7jri8IAvAfQ4Qiww3Ef-D_qn6rgBq_aNWhHQHcu6yGkEvyoSRIJwxCm6knHQJWLW7CpEoFnBQiTuc7SlEYqCAKa0kX1mWmrUkp_u9jo_3NGZ9fF8qb8MTcwjuPB1bdYeZ7zdEm3vteL4eC82x_HV43s_q1ftRf3L7q9YeWtvYM1m-u4Ru2e6HgyPkslaCqfmVbwTcej41tgVe7KYFUgQEtR1T1C62PV5WVP0iunw_R9LtYy7AemdqtCrKro025h7d8UXh2GKLebYEC599CxLGQHQvG4-yWefK0fQ5GOo4o16p9sci-rAMH9mf9m2zxc0BXccZlqlbKn9StTZvQYnWyTPPFlTZ8nqCXyp-iR3pVgLfNXMGQaf5ixZ2hU8eZD7yPe5QxucgazO0xzbDiDm5zBNWdwzZkT9P1TPDr_bOn2HBZ3g6i0OoySzCM2Z2nmZ7IOH6UZmNcCXtMsdAmLMpkH7UcZJ47HbJn6mMKCu5QxL7Xd5-goL3LxAmFXOB1KeBqFsEHmDuzCg4yApRsQJhwW-G3kmoVNuK5dL1uozBITpDhNFByJhCORsZqe20ZWfdZC1W458H1iMEu0_ansygRId-DM0x2I6-kMw9rorcE8AfktH8rRXBTrVSIVpMwOt6OXhy7yCj3Y3mGv0VG5XIs36D7flDer5anm7i9ay7sX
linkProvider	Elsevier
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=proceeding&rft.title=Neurocomputing+%28Amsterdam%29&rft.atitle=ADEMO%2FD%3A+Multiobjective+optimization+by+an+adaptive+differential+evolution+algorithm&rft.au=VENSKE%2C+Sandra+M&rft.au=GONCALVES%2C+Richard+A&rft.au=DELGADO%2C+Myriam+R&rft.date=2014-03-15&rft.pub=Elsevier&rft.issn=0925-2312&rft.volume=127&rft.spage=65&rft.epage=77&rft_id=info:doi/10.1016%2Fj.neucom.2013.06.043&rft.externalDBID=n%2Fa&rft.externalDocID=28284313
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0925-2312&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0925-2312&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0925-2312&client=summon