DCDG-EA: Dynamic convergence–diversity guided evolutionary algorithm for many-objective optimization
•DCDG-EA algorithm uses reference vector decomposition to solve MaOPs.•CDOS selects an appropriate operator to generate offspring.•CDIS strategy simultaneously considers the convergence and diversity of solutions. Maintaining a good balance between the convergence and the diversity is particularly c...
Uložené v:
| Vydané v: | Expert systems with applications Ročník 118; s. 35 - 51 |
|---|---|
| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York
Elsevier Ltd
15.03.2019
Elsevier BV |
| Predmet: | |
| ISSN: | 0957-4174, 1873-6793 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | •DCDG-EA algorithm uses reference vector decomposition to solve MaOPs.•CDOS selects an appropriate operator to generate offspring.•CDIS strategy simultaneously considers the convergence and diversity of solutions.
Maintaining a good balance between the convergence and the diversity is particularly crucial for the performance of the evolutionary algorithms (EAs). However, the traditional multi-objective evolutionary algorithms, which have shown their competitive performance with a variety of practical problems involving two or three objectives, face significant challenges in case of problems with more than three objectives, namely many-objective optimization problems (MaOPs). This paper proposes a dynamic convergence–diversity guided evolutionary algorithm, namely (DCDG-EA) for MaOPs by employing the decomposition technique. Besides, the objective space of MaOPs is divided into K subspaces by a set of uniformly distributed reference vectors. Each subspace has its own subpopulation and evolves in parallel with the other subspaces. In DCDG-EA, the balance between the convergence and the diversity is achieved through the convergence–diversity based operator selection (CDOS) strategy and convergence–diversity based individual selection (CDIS) strategy. In CDOS, for each operator of the set of operators, a selection probability is assigned which is related to its convergence and diversity capabilities. Based on the attributed selection probabilities, an appropriate operator is selected to generate the offsprings. Furthermore, CDIS is used which allows to greatly overcome the inefficiency of the Pareto dominance approaches. It updates each subpopulation by using two independent distance measures that represent the convergence and the control diversity, respectively. The experimental results on DTLZ and WFG benchmark problems with up to 15 objectives show that our algorithm is highly competitive comparing with the four state-of-the-art evolutionary algorithms in terms of convergence and diversity. |
|---|---|
| AbstractList | Maintaining a good balance between the convergence and the diversity is particularly crucial for the performance of the evolutionary algorithms (EAs). However, the traditional multi-objective evolutionary algorithms, which have shown their competitive performance with a variety of practical problems involving two or three objectives, face significant challenges in case of problems with more than three objectives, namely many-objective optimization problems (MaOPs). This paper proposes a dynamic convergence–diversity guided evolutionary algorithm, namely (DCDG-EA) for MaOPs by employing the decomposition technique. Besides, the objective space of MaOPs is divided into K subspaces by a set of uniformly distributed reference vectors. Each subspace has its own subpopulation and evolves in parallel with the other subspaces. In DCDG-EA, the balance between the convergence and the diversity is achieved through the convergence–diversity based operator selection (CDOS) strategy and convergence–diversity based individual selection (CDIS) strategy. In CDOS, for each operator of the set of operators, a selection probability is assigned which is related to its convergence and diversity capabilities. Based on the attributed selection probabilities, an appropriate operator is selected to generate the offsprings. Furthermore, CDIS is used which allows to greatly overcome the inefficiency of the Pareto dominance approaches. It updates each subpopulation by using two independent distance measures that represent the convergence and the control diversity, respectively. The experimental results on DTLZ and WFG benchmark problems with up to 15 objectives show that our algorithm is highly competitive comparing with the four state-of-the-art evolutionary algorithms in terms of convergence and diversity. •DCDG-EA algorithm uses reference vector decomposition to solve MaOPs.•CDOS selects an appropriate operator to generate offspring.•CDIS strategy simultaneously considers the convergence and diversity of solutions. Maintaining a good balance between the convergence and the diversity is particularly crucial for the performance of the evolutionary algorithms (EAs). However, the traditional multi-objective evolutionary algorithms, which have shown their competitive performance with a variety of practical problems involving two or three objectives, face significant challenges in case of problems with more than three objectives, namely many-objective optimization problems (MaOPs). This paper proposes a dynamic convergence–diversity guided evolutionary algorithm, namely (DCDG-EA) for MaOPs by employing the decomposition technique. Besides, the objective space of MaOPs is divided into K subspaces by a set of uniformly distributed reference vectors. Each subspace has its own subpopulation and evolves in parallel with the other subspaces. In DCDG-EA, the balance between the convergence and the diversity is achieved through the convergence–diversity based operator selection (CDOS) strategy and convergence–diversity based individual selection (CDIS) strategy. In CDOS, for each operator of the set of operators, a selection probability is assigned which is related to its convergence and diversity capabilities. Based on the attributed selection probabilities, an appropriate operator is selected to generate the offsprings. Furthermore, CDIS is used which allows to greatly overcome the inefficiency of the Pareto dominance approaches. It updates each subpopulation by using two independent distance measures that represent the convergence and the control diversity, respectively. The experimental results on DTLZ and WFG benchmark problems with up to 15 objectives show that our algorithm is highly competitive comparing with the four state-of-the-art evolutionary algorithms in terms of convergence and diversity. |
| Author | Jiang, Shilong Nouioua, Mourad Gu, Yu Lin, Ke Li, Zhiyong |
| Author_xml | – sequence: 1 givenname: Zhiyong orcidid: 0000-0001-9720-5915 surname: Li fullname: Li, Zhiyong email: zhiyong.li@hnu.edu.cn organization: Key Laboratory for Embedded and Network Computing of Hunan Province 410082, College of Computer Science and Electronic Engineering of Hunan University, Changsha 410082, China – sequence: 2 givenname: Ke surname: Lin fullname: Lin, Ke email: kelin_0808@hnu.edu.cn organization: Key Laboratory for Embedded and Network Computing of Hunan Province 410082, College of Computer Science and Electronic Engineering of Hunan University, Changsha 410082, China – sequence: 3 givenname: Mourad surname: Nouioua fullname: Nouioua, Mourad email: mouradnouioua@hnu.edu.cn organization: Key Laboratory for Embedded and Network Computing of Hunan Province 410082, College of Computer Science and Electronic Engineering of Hunan University, Changsha 410082, China – sequence: 4 givenname: Shilong surname: Jiang fullname: Jiang, Shilong email: jiangshilong03@126.com organization: PKU-HKUST Shenzhen-HongKong Institution, Shenzhen 518057, China – sequence: 5 givenname: Yu surname: Gu fullname: Gu, Yu email: guyu@mail.buct.edu.cn organization: Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China |
| BookMark | eNp9kMtO4zAUhi3UkSjMvACrSKyT8SWNY8QGteUiVZrNzNpy7JPiqImL7RaVFe_AG86T4FBWLFgdHen_zuU7Q5PBDYDQBcEFwaT63RUQnlVBMakLLApMZydoSmrO8ooLNkFTLGY8LwkvT9FZCB3GhGPMp6hdzBd3-fLmKlscBtVbnWk37MGvYdDw__XN2NQEGw_ZemcNmAz2brOL1g3KHzK1WTtv42Oftc5nvRoOuWs60DFRmdtG29sXNYZ_oh-t2gT49VnP0b_b5d_5fb76c_cwv1nlmtUk5nXbqJrzCremLoFqww01hEHFhGkUowQDMIJLU5W60ayhom5US7igomEKMDtHl8e5W--edhCi7NzOD2mlpGTGy6qkgqZUfUxp70Lw0Ept48ed0Su7kQTL0ars5GhVjlYlFjJZTSj9gm697ZOL76HrIwTp9b0FL4O2o2BjfZIljbPf4e_ubpZE |
| CitedBy_id | crossref_primary_10_1007_s12065_021_00698_4 crossref_primary_10_1007_s00500_020_05457_8 crossref_primary_10_7717_peerj_cs_1710 crossref_primary_10_1155_2023_1572996 crossref_primary_10_1016_j_swevo_2022_101145 crossref_primary_10_1016_j_eswa_2021_115654 crossref_primary_10_1007_s10489_022_03883_9 |
| Cites_doi | 10.1109/4235.996017 10.1016/j.asoc.2015.06.036 10.1016/j.eswa.2015.07.043 10.1109/TEVC.2012.2185847 10.1029/2009WR008121 10.1162/EVCO_a_00009 10.1109/TSMCA.2004.824873 10.1109/TSMCB.2008.926329 10.1287/opre.26.1.127 10.1016/j.eswa.2013.09.012 10.1109/TEVC.2012.2227145 10.1007/11732242_71 10.1109/TEVC.2007.892759 10.1016/j.ejor.2006.08.008 10.1137/S1052623496307510 10.1109/TEVC.2013.2258025 10.1109/TEVC.2013.2281535 10.1109/4235.797969 10.1109/TEVC.2005.861417 10.1109/TEVC.2013.2281533 10.1109/TEVC.2016.2549267 10.1109/MCI.2017.2742868 10.1109/TEVC.2003.810761 10.1109/TEVC.2010.2077298 10.1109/TCYB.2016.2638902 10.1162/EVCO_a_00075 10.1023/A:1008202821328 10.1162/106365602760234108 |
| ContentType | Journal Article |
| Copyright | 2018 Elsevier Ltd Copyright Elsevier BV Mar 15, 2019 |
| Copyright_xml | – notice: 2018 Elsevier Ltd – notice: Copyright Elsevier BV Mar 15, 2019 |
| DBID | AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D |
| DOI | 10.1016/j.eswa.2018.09.025 |
| DatabaseName | CrossRef 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 |
| EISSN | 1873-6793 |
| EndPage | 51 |
| ExternalDocumentID | 10_1016_j_eswa_2018_09_025 S095741741830602X |
| GroupedDBID | --K --M .DC .~1 0R~ 13V 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN 9JO AAAKF AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AARIN AAXUO AAYFN ABBOA ABFNM ABMAC ABMVD ABUCO ABYKQ ACDAQ ACGFS ACHRH ACNTT ACRLP ACZNC ADBBV ADEZE ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGJBL AGUBO AGUMN AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV ALEQD ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM AXJTR BJAXD BKOJK BLXMC BNSAS CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX IHE J1W JJJVA KOM LG9 LY1 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 RIG ROL RPZ SDF SDG SDP SDS SES SPC SPCBC SSB SSD SSL SST SSV SSZ T5K TN5 ~G- 29G 9DU AAAKG AAQXK AATTM AAXKI AAYWO AAYXX ABJNI ABKBG ABUFD ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS FEDTE FGOYB G-2 HLZ HVGLF HZ~ R2- SBC SET SEW WUQ XPP ZMT ~HD 7SC 8FD AFXIZ AGCQF AGRNS JQ2 L7M L~C L~D SSH |
| ID | FETCH-LOGICAL-c381t-8fba87760fd84e2cd7d2d13e639dba3210ee3104d64cbc3b298baf17929b3ae03 |
| ISICitedReferencesCount | 10 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000451653400003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0957-4174 |
| IngestDate | Sun Jul 13 05:35:47 EDT 2025 Sat Nov 29 06:14:29 EST 2025 Tue Nov 18 22:10:05 EST 2025 Fri Feb 23 02:24:26 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Pareto optimality Decomposition Many-objective optimization Evolutionary algorithm Diversity Convergence |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c381t-8fba87760fd84e2cd7d2d13e639dba3210ee3104d64cbc3b298baf17929b3ae03 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-9720-5915 |
| PQID | 2157464292 |
| PQPubID | 2045477 |
| PageCount | 17 |
| ParticipantIDs | proquest_journals_2157464292 crossref_citationtrail_10_1016_j_eswa_2018_09_025 crossref_primary_10_1016_j_eswa_2018_09_025 elsevier_sciencedirect_doi_10_1016_j_eswa_2018_09_025 |
| PublicationCentury | 2000 |
| PublicationDate | 2019-03-15 |
| PublicationDateYYYYMMDD | 2019-03-15 |
| PublicationDate_xml | – month: 03 year: 2019 text: 2019-03-15 day: 15 |
| PublicationDecade | 2010 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | Expert systems with applications |
| PublicationYear | 2019 |
| Publisher | Elsevier Ltd Elsevier BV |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier BV |
| References | Basseur, Zitzler (bib0002) 2006; 2 While, Bradstreet, Barone (bib0033) 2012; 16 Dhall, Liu (bib0010) 1978; 26 Kukkonen, Lampinen (bib0017) 2007 Nguyen, Li, Zhang, Truong (bib0025) 2014; 41 Huband, Hingston, Barone, While (bib0014) 2006; 10 Li, Wang, Yan, Li (bib0021) 2015; 42 Li, Tang, Li, Yao (bib0019) 2016; 20 Zou, Chen, Liu, Kang (bib0039) 2008; 38 Das, Dennis (bib0005) 2000; 8 Laumanns, Thiele, Deb, Zitzler (bib0018) 2002; 10 Deb, Agrawal (bib0006) 1994; 9 He, Yen, Zhang (bib0013) 2014; 18 Saxena, Duro, Tiwari, Deb (bib0028) 2013; 17 Zitzler, Knzli (bib0037) 2004 Thierens (bib0031) 2005 Sheskin (bib0029) 2004 Liu, Gong, Jing, Jin (bib0023) 2017; 47 Hadka, Reed (bib0012) 2013; 21 Zitzler, Thiele (bib0038) 1999; 3 Farina, Amato (bib0011) 2004; 34 Bosman, Thierens (bib0004) 2003; 7 Beume, Naujoks, Emmerich (bib0003) 2007; 181 Hughes (bib0015) 2004; Vol. 4 Storn, Price (bib0030) 1997; 114 Bader, Zitzler (bib0001) 2011; 19 Deb, Thiele, Laumanns, Zitzler (bib0009) 2001 Zhang, Li (bib0036) 2007; 11 Xiao, Wang, Bi (bib0034) 2014; 29 Sato, Aguirre, Tanaka (bib0027) 2011 Yang, Li, Liu, Zheng (bib0035) 2013; 17 Deb, Pratap, Agarwal, Meyarivan (bib0008) 2002; 6 Tian, Cheng, Zhang, Jin (bib0032) 2017; 12 Li, Nguyen, Chen, Truong (bib0020) 2015; 35 LOpez, Coello, Oyama, Fujii (bib0024) 2013 Pez Jaimes, Coello Coello, Chakraborty (bib0026) 2008 Kasprzyk, Reed, Kirsch, Characklis (bib0016) 2009; 45 Deb, Jain (bib0007) 2014; 18 Liu, Gu, Zhang (bib0022) 2014; 18 Saxena (10.1016/j.eswa.2018.09.025_bib0028) 2013; 17 Dhall (10.1016/j.eswa.2018.09.025_bib0010) 1978; 26 Bosman (10.1016/j.eswa.2018.09.025_bib0004) 2003; 7 Li (10.1016/j.eswa.2018.09.025_bib0019) 2016; 20 LOpez (10.1016/j.eswa.2018.09.025_bib0024) 2013 Pez Jaimes (10.1016/j.eswa.2018.09.025_bib0026) 2008 Tian (10.1016/j.eswa.2018.09.025_bib0032) 2017; 12 While (10.1016/j.eswa.2018.09.025_bib0033) 2012; 16 Zitzler (10.1016/j.eswa.2018.09.025_bib0038) 1999; 3 Zhang (10.1016/j.eswa.2018.09.025_bib0036) 2007; 11 Nguyen (10.1016/j.eswa.2018.09.025_bib0025) 2014; 41 Zitzler (10.1016/j.eswa.2018.09.025_bib0037) 2004 Beume (10.1016/j.eswa.2018.09.025_bib0003) 2007; 181 Storn (10.1016/j.eswa.2018.09.025_bib0030) 1997; 114 Yang (10.1016/j.eswa.2018.09.025_bib0035) 2013; 17 Hadka (10.1016/j.eswa.2018.09.025_bib0012) 2013; 21 Kasprzyk (10.1016/j.eswa.2018.09.025_bib0016) 2009; 45 Basseur (10.1016/j.eswa.2018.09.025_bib0002) 2006; 2 Li (10.1016/j.eswa.2018.09.025_bib0020) 2015; 35 Das (10.1016/j.eswa.2018.09.025_bib0005) 2000; 8 He (10.1016/j.eswa.2018.09.025_bib0013) 2014; 18 Sato (10.1016/j.eswa.2018.09.025_bib0027) 2011 Deb (10.1016/j.eswa.2018.09.025_bib0009) 2001 Sheskin (10.1016/j.eswa.2018.09.025_bib0029) 2004 Laumanns (10.1016/j.eswa.2018.09.025_bib0018) 2002; 10 Thierens (10.1016/j.eswa.2018.09.025_bib0031) 2005 Li (10.1016/j.eswa.2018.09.025_bib0021) 2015; 42 Xiao (10.1016/j.eswa.2018.09.025_bib0034) 2014; 29 Huband (10.1016/j.eswa.2018.09.025_bib0014) 2006; 10 Bader (10.1016/j.eswa.2018.09.025_bib0001) 2011; 19 Farina (10.1016/j.eswa.2018.09.025_bib0011) 2004; 34 Hughes (10.1016/j.eswa.2018.09.025_bib0015) 2004; Vol. 4 Liu (10.1016/j.eswa.2018.09.025_bib0023) 2017; 47 Zou (10.1016/j.eswa.2018.09.025_bib0039) 2008; 38 Deb (10.1016/j.eswa.2018.09.025_bib0006) 1994; 9 Kukkonen (10.1016/j.eswa.2018.09.025_bib0017) 2007 Deb (10.1016/j.eswa.2018.09.025_bib0008) 2002; 6 Deb (10.1016/j.eswa.2018.09.025_bib0007) 2014; 18 Liu (10.1016/j.eswa.2018.09.025_bib0022) 2014; 18 |
| References_xml | – year: 2004 ident: bib0037 article-title: Indicator-based selection in multiobjective search – volume: 45 year: 2009 ident: bib0016 article-title: Managing population and drought risks using many-objective water portfolio planning under uncertainty publication-title: Water Resources Research – volume: 181 start-page: 1653 year: 2007 end-page: 1669 ident: bib0003 article-title: SMS-EMOA: Multiobjective selection based on dominated hypervolume publication-title: European Journal of Operational Research – volume: 6 start-page: 182 year: 2002 end-page: 197 ident: bib0008 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Transactions on Evolutionary Computation – volume: 2 start-page: 727 year: 2006 end-page: 739 ident: bib0002 article-title: A preliminary study on handling uncertainty in indicator-based multiobjective optimization publication-title: Lecture Notes in Computer Science – volume: 18 start-page: 269 year: 2014 end-page: 285 ident: bib0013 article-title: Fuzzy-based Pareto optimality for many-objective evolutionary algorithms publication-title: IEEE Transactions on Evolutionary Computation – volume: 47 start-page: 2689 year: 2017 end-page: 2702 ident: bib0023 article-title: A many-objective evolutionary algorithm using a one-by-one selection strategy publication-title: IEEE Transactions on Cybernetics – volume: 17 start-page: 721 year: 2013 end-page: 736 ident: bib0035 article-title: A grid-based evolutionary algorithm for many-objective optimization publication-title: IEEE Transactions on Evolutionary Computation – volume: 10 start-page: 263 year: 2002 ident: bib0018 article-title: Combining convergence and diversity in evolutionary multiobjective optimization publication-title: Evolutionary Computation – volume: 17 start-page: 77 year: 2013 end-page: 99 ident: bib0028 article-title: Objective reduction in many-objective optimization: Linear and nonlinear algorithms publication-title: IEEE Transactions on Evolutionary Computation – volume: 11 start-page: 712 year: 2007 end-page: 731 ident: bib0036 article-title: MOEA/D: A multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Transactions on Evolutionary Computation – volume: 42 start-page: 8881 year: 2015 end-page: 8895 ident: bib0021 article-title: PSABC: A hybrid algorithm based on particle swarm and artificial bee colony for high-dimensional optimization problems publication-title: Expert Systems with Applications – start-page: 673 year: 2008 end-page: 680 ident: bib0026 article-title: Objective reduction using a feature selection technique publication-title: Proceedings of the 2008 genetic and evolutionary computation conference, GECCO 2008 – volume: 38 start-page: 1402 year: 2008 end-page: 1412 ident: bib0039 article-title: A new evolutionary algorithm for solving many-objective optimization problems publication-title: IEEE Transactions on Systems Man & Cybernetics Part B: Cybernetics – volume: 10 start-page: 477 year: 2006 end-page: 506 ident: bib0014 article-title: A review of multiobjective test problems and a scalable test problem toolkit publication-title: IEEE Transactions on Evolutionary Computation – volume: 9 start-page: 115 year: 1994 end-page: 148 ident: bib0006 article-title: Simulated binary crossover for continuous search space publication-title: Complex Systems – volume: 41 start-page: 2134 year: 2014 end-page: 2143 ident: bib0025 article-title: A hybrid algorithm based on particle swarm and chemical reaction optimization publication-title: Expert Systems with Applications – volume: 35 start-page: 525 year: 2015 end-page: 540 ident: bib0020 article-title: A hybrid algorithm based on particle swarm and chemical reaction optimization for multi-object problems publication-title: Applied Soft Computing – volume: 29 start-page: 182 year: 2014 end-page: 197 ident: bib0034 article-title: Multi-objective evolutionary algorithm based on improved k-dominated sorting publication-title: Control and Decision – volume: 21 start-page: 231 year: 2013 end-page: 259 ident: bib0012 article-title: Borg: An auto-adaptive many-objective evolutionary computing framework publication-title: Evolutionary Computation – volume: 18 start-page: 577 year: 2014 end-page: 601 ident: bib0007 article-title: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints publication-title: IEEE Transactions on Evolutionary Computation – year: 2013 ident: bib0024 article-title: An alternative preference relation to deal with many-objective optimization problems – start-page: 105 year: 2001 end-page: 145 ident: bib0009 article-title: Scalable test problems for evolutionary multiobjective optimization publication-title: Evolutionary Multiobjective Optimization – start-page: 3983 year: 2007 end-page: 3990 ident: bib0017 article-title: Ranking-dominance and many-objective optimization publication-title: Proceedings of the IEEE congress on evolutionary computation, CEC 2007 – volume: 34 start-page: 315 year: 2004 end-page: 326 ident: bib0011 article-title: A fuzzy definition of optimality for many-criteria optimization problems publication-title: IEEE Transactions on Systems Man and Cybernetics – Part A Systems and Humans – volume: 18 start-page: 450 year: 2014 end-page: 455 ident: bib0022 article-title: Decomposition of a multiobjective optimization problem into a number of simple multiobjective subproblems publication-title: IEEE Transactions on Evolutionary Computation – start-page: 1539 year: 2005 end-page: 1546 ident: bib0031 article-title: An adaptive pursuit strategy for allocating operator probabilities publication-title: Proceedings of the 2005 genetic and evolutionary computation conference, GECCO 2005 – volume: 16 start-page: 86 year: 2012 end-page: 95 ident: bib0033 article-title: A fast way of calculating exact hypervolumes publication-title: IEEE Transactions on Evolutionary Computation – volume: 8 start-page: 631 year: 2000 end-page: 657 ident: bib0005 article-title: Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems publication-title: Siam Journal on Optimization – volume: 26 start-page: 127 year: 1978 end-page: 140 ident: bib0010 article-title: On a real-time scheduling problem publication-title: Operations Research – volume: 12 start-page: 73 year: 2017 end-page: 87 ident: bib0032 article-title: PlatEMO: A Matlab platform for evolutionary multi-objective optimization [educational forum] publication-title: IEEE Computational Intelligence Magazine – start-page: 91 year: 2011 end-page: 105 ident: bib0027 article-title: Genetic diversity and effective crossover in evolutionary many-objective optimization publication-title: Proceedings of the 2011 international conference on learning and intelligent optimization – volume: 114 start-page: 341 year: 1997 end-page: 359 ident: bib0030 article-title: De-a simple and efficient heuristic for global optimization over continuous space publication-title: Journal of Global Optimization – volume: 7 start-page: 174 year: 2003 end-page: 188 ident: bib0004 article-title: The balance between proximity and diversity in multiobjective evolutionary algorithms publication-title: IEEE Transactions on Evolutionary Computation – volume: 3 start-page: 257 year: 1999 end-page: 271 ident: bib0038 article-title: Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach publication-title: IEEE Transactions on Evolutionary Computation – volume: 19 start-page: 45 year: 2011 end-page: 76 ident: bib0001 article-title: Hype: An algorithm for fast hypervolume-based many-objective optimization publication-title: Evolutionary Computation – volume: Vol. 4 start-page: 2678 year: 2004 end-page: 2684 ident: bib0015 article-title: Multiple single objective Pareto sampling publication-title: The 2003 Congress on Evolutionary Computation, CEC ’03 – volume: 20 start-page: 924 year: 2016 end-page: 938 ident: bib0019 article-title: Stochastic ranking algorithm for many-objective optimization based on multiple indicators publication-title: IEEE Transactions on Evolutionary Computation – year: 2004 ident: bib0029 article-title: Handbook of parametric and nonparametric statistical procedures – volume: 6 start-page: 182 issue: 2 year: 2002 ident: 10.1016/j.eswa.2018.09.025_bib0008 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.996017 – volume: 35 start-page: 525 issue: C year: 2015 ident: 10.1016/j.eswa.2018.09.025_bib0020 article-title: A hybrid algorithm based on particle swarm and chemical reaction optimization for multi-object problems publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2015.06.036 – volume: 42 start-page: 8881 issue: 22 year: 2015 ident: 10.1016/j.eswa.2018.09.025_bib0021 article-title: PSABC: A hybrid algorithm based on particle swarm and artificial bee colony for high-dimensional optimization problems publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2015.07.043 – volume: 17 start-page: 77 issue: 1 year: 2013 ident: 10.1016/j.eswa.2018.09.025_bib0028 article-title: Objective reduction in many-objective optimization: Linear and nonlinear algorithms publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2012.2185847 – volume: Vol. 4 start-page: 2678 year: 2004 ident: 10.1016/j.eswa.2018.09.025_bib0015 article-title: Multiple single objective Pareto sampling – volume: 45 issue: 12 year: 2009 ident: 10.1016/j.eswa.2018.09.025_bib0016 article-title: Managing population and drought risks using many-objective water portfolio planning under uncertainty publication-title: Water Resources Research doi: 10.1029/2009WR008121 – volume: 19 start-page: 45 issue: 19 year: 2011 ident: 10.1016/j.eswa.2018.09.025_bib0001 article-title: Hype: An algorithm for fast hypervolume-based many-objective optimization publication-title: Evolutionary Computation doi: 10.1162/EVCO_a_00009 – volume: 34 start-page: 315 issue: 3 year: 2004 ident: 10.1016/j.eswa.2018.09.025_bib0011 article-title: A fuzzy definition of optimality for many-criteria optimization problems publication-title: IEEE Transactions on Systems Man and Cybernetics – Part A Systems and Humans doi: 10.1109/TSMCA.2004.824873 – volume: 38 start-page: 1402 issue: 5 year: 2008 ident: 10.1016/j.eswa.2018.09.025_bib0039 article-title: A new evolutionary algorithm for solving many-objective optimization problems publication-title: IEEE Transactions on Systems Man & Cybernetics Part B: Cybernetics doi: 10.1109/TSMCB.2008.926329 – volume: 26 start-page: 127 issue: 1 year: 1978 ident: 10.1016/j.eswa.2018.09.025_bib0010 article-title: On a real-time scheduling problem publication-title: Operations Research doi: 10.1287/opre.26.1.127 – volume: 41 start-page: 2134 issue: 5 year: 2014 ident: 10.1016/j.eswa.2018.09.025_bib0025 article-title: A hybrid algorithm based on particle swarm and chemical reaction optimization publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2013.09.012 – volume: 17 start-page: 721 issue: 5 year: 2013 ident: 10.1016/j.eswa.2018.09.025_bib0035 article-title: A grid-based evolutionary algorithm for many-objective optimization publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2012.2227145 – start-page: 3983 year: 2007 ident: 10.1016/j.eswa.2018.09.025_bib0017 article-title: Ranking-dominance and many-objective optimization – volume: 2 start-page: 727 issue: 3 year: 2006 ident: 10.1016/j.eswa.2018.09.025_bib0002 article-title: A preliminary study on handling uncertainty in indicator-based multiobjective optimization publication-title: Lecture Notes in Computer Science doi: 10.1007/11732242_71 – volume: 11 start-page: 712 issue: 6 year: 2007 ident: 10.1016/j.eswa.2018.09.025_bib0036 article-title: MOEA/D: A multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2007.892759 – volume: 181 start-page: 1653 issue: 3 year: 2007 ident: 10.1016/j.eswa.2018.09.025_bib0003 article-title: SMS-EMOA: Multiobjective selection based on dominated hypervolume publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2006.08.008 – volume: 8 start-page: 631 issue: 3 year: 2000 ident: 10.1016/j.eswa.2018.09.025_bib0005 article-title: Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems publication-title: Siam Journal on Optimization doi: 10.1137/S1052623496307510 – volume: 18 start-page: 269 issue: 2 year: 2014 ident: 10.1016/j.eswa.2018.09.025_bib0013 article-title: Fuzzy-based Pareto optimality for many-objective evolutionary algorithms publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2013.2258025 – volume: 29 start-page: 182 issue: 12 year: 2014 ident: 10.1016/j.eswa.2018.09.025_bib0034 article-title: Multi-objective evolutionary algorithm based on improved k-dominated sorting publication-title: Control and Decision – start-page: 91 year: 2011 ident: 10.1016/j.eswa.2018.09.025_bib0027 article-title: Genetic diversity and effective crossover in evolutionary many-objective optimization – start-page: 1539 year: 2005 ident: 10.1016/j.eswa.2018.09.025_bib0031 article-title: An adaptive pursuit strategy for allocating operator probabilities – year: 2004 ident: 10.1016/j.eswa.2018.09.025_bib0037 – volume: 18 start-page: 577 issue: 4 year: 2014 ident: 10.1016/j.eswa.2018.09.025_bib0007 article-title: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2013.2281535 – volume: 3 start-page: 257 issue: 4 year: 1999 ident: 10.1016/j.eswa.2018.09.025_bib0038 article-title: Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.797969 – volume: 10 start-page: 477 issue: 5 year: 2006 ident: 10.1016/j.eswa.2018.09.025_bib0014 article-title: A review of multiobjective test problems and a scalable test problem toolkit publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2005.861417 – volume: 18 start-page: 450 issue: 3 year: 2014 ident: 10.1016/j.eswa.2018.09.025_bib0022 article-title: Decomposition of a multiobjective optimization problem into a number of simple multiobjective subproblems publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2013.2281533 – volume: 20 start-page: 924 issue: 6 year: 2016 ident: 10.1016/j.eswa.2018.09.025_bib0019 article-title: Stochastic ranking algorithm for many-objective optimization based on multiple indicators publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2016.2549267 – volume: 12 start-page: 73 issue: 4 year: 2017 ident: 10.1016/j.eswa.2018.09.025_bib0032 article-title: PlatEMO: A Matlab platform for evolutionary multi-objective optimization [educational forum] publication-title: IEEE Computational Intelligence Magazine doi: 10.1109/MCI.2017.2742868 – year: 2013 ident: 10.1016/j.eswa.2018.09.025_bib0024 – volume: 7 start-page: 174 issue: 2 year: 2003 ident: 10.1016/j.eswa.2018.09.025_bib0004 article-title: The balance between proximity and diversity in multiobjective evolutionary algorithms publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2003.810761 – volume: 16 start-page: 86 issue: 1 year: 2012 ident: 10.1016/j.eswa.2018.09.025_bib0033 article-title: A fast way of calculating exact hypervolumes publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2010.2077298 – start-page: 673 year: 2008 ident: 10.1016/j.eswa.2018.09.025_bib0026 article-title: Objective reduction using a feature selection technique – volume: 47 start-page: 2689 issue: 9 year: 2017 ident: 10.1016/j.eswa.2018.09.025_bib0023 article-title: A many-objective evolutionary algorithm using a one-by-one selection strategy publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2016.2638902 – volume: 21 start-page: 231 issue: 2 year: 2013 ident: 10.1016/j.eswa.2018.09.025_bib0012 article-title: Borg: An auto-adaptive many-objective evolutionary computing framework publication-title: Evolutionary Computation doi: 10.1162/EVCO_a_00075 – start-page: 105 year: 2001 ident: 10.1016/j.eswa.2018.09.025_bib0009 article-title: Scalable test problems for evolutionary multiobjective optimization publication-title: Evolutionary Multiobjective Optimization – year: 2004 ident: 10.1016/j.eswa.2018.09.025_bib0029 – volume: 114 start-page: 341 issue: 4 year: 1997 ident: 10.1016/j.eswa.2018.09.025_bib0030 article-title: De-a simple and efficient heuristic for global optimization over continuous space publication-title: Journal of Global Optimization doi: 10.1023/A:1008202821328 – volume: 9 start-page: 115 issue: 3 year: 1994 ident: 10.1016/j.eswa.2018.09.025_bib0006 article-title: Simulated binary crossover for continuous search space publication-title: Complex Systems – volume: 10 start-page: 263 issue: 3 year: 2002 ident: 10.1016/j.eswa.2018.09.025_bib0018 article-title: Combining convergence and diversity in evolutionary multiobjective optimization publication-title: Evolutionary Computation doi: 10.1162/106365602760234108 |
| SSID | ssj0017007 |
| Score | 2.3266835 |
| Snippet | •DCDG-EA algorithm uses reference vector decomposition to solve MaOPs.•CDOS selects an appropriate operator to generate offspring.•CDIS strategy simultaneously... Maintaining a good balance between the convergence and the diversity is particularly crucial for the performance of the evolutionary algorithms (EAs). However,... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 35 |
| SubjectTerms | Algorithms Convergence Decomposition Distance measurement Diversity Evolutionary algorithm Evolutionary algorithms Many-objective optimization Multiple objective analysis Optimization Pareto optimality State of the art Subspaces |
| Title | DCDG-EA: Dynamic convergence–diversity guided evolutionary algorithm for many-objective optimization |
| URI | https://dx.doi.org/10.1016/j.eswa.2018.09.025 https://www.proquest.com/docview/2157464292 |
| Volume | 118 |
| WOSCitedRecordID | wos000451653400003&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: 1873-6793 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017007 issn: 0957-4174 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3LbtNAFB2FlgUb3qiFgmbBrprK77HZVU14SVRIFCliM_I83DpK7CpxQtnxD3wA_8aXcMfzSFpEBUhsnGgUO5O5J3eur8-5F6HnVZqXokglgdecJGEQE57qNFxAy1DSmJeq6ptN0OPjfDwu3g8G350WZjWlTZNfXBTn_9XUMAbG1tLZvzC3vygMwHswOhzB7HD8I8MPj4avyKjPmw9Nu3lDLe9VlsqRG2LpCRmny1pC2KlWdlqaR1dOT9t53Z3NehriDDwGafnEOMf9FtzMzOo3L2X2ddnkzhaHdrK5jQfknvzTMwg-ndVfWrtv9oON0Qn59HS7rNtlH9u-g99dSk_2qV2OW7dhsFewiQutlYqJkW6abJpT1KzpSyYtSQEypnPPgTJOOacxyajppOi9tnXbxu-akid2BzcVbH_ZG0yaYnKgFp91wakw7wvcRul6J_T8xA96GnoW4PCCLIjGN9B2RNMC3Ob24ZvR-K1_UEUDo8h307a6LEMhvPpNv4t9rkQBfWhzchfdtvck-NBg6R4aqOY-uuP6fWDr_h-gykLrBbbAwhvA-vH1m4cUNpDCm5DCHlIYIIUvQwpvQuoh-vhydHL0mtg-HURAvNeRvOJlTmkWVDJPVCQklZEMYwXBr-SlFokpBXcRicwSwUXMoyLnZQU7QVTwuFRB_AhtNW2jdhDmqkirAhZawD7BE1losTRPkiCEYE6EYheFbgWZsEXsdS-VKXNsxQnTq870qrOgYLDqu2jfn3NuSrhc--nUGYbZINQElwxwdO15e86KzHqDBYN4miaZ7gj3-B8v-wTdWv939tBWN1-qp-imWHX1Yv7MovEnwzK6mA |
| 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=article&rft.atitle=DCDG-EA%3A+Dynamic+convergence%E2%80%93diversity+guided+evolutionary+algorithm+for+many-objective+optimization&rft.jtitle=Expert+systems+with+applications&rft.au=Li%2C+Zhiyong&rft.au=Lin%2C+Ke&rft.au=Nouioua%2C+Mourad&rft.au=Jiang%2C+Shilong&rft.date=2019-03-15&rft.pub=Elsevier+Ltd&rft.issn=0957-4174&rft.eissn=1873-6793&rft.volume=118&rft.spage=35&rft.epage=51&rft_id=info:doi/10.1016%2Fj.eswa.2018.09.025&rft.externalDocID=S095741741830602X |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0957-4174&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0957-4174&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0957-4174&client=summon |