A coevolution algorithm based on two-staged strategy for constrained multi-objective problems
Constrained Multiobjective Problem (CMOP) is widely used in engineering applications, but the current constrained Multiobjective Optimization algorithms (CMOEA) often fails to effectively balance convergence and diversity. For this purpose, a two-stage co-evolution constrained multi-objective optimi...
Gespeichert in:
| Veröffentlicht in: | Applied intelligence (Dordrecht, Netherlands) Jg. 52; H. 15; S. 17954 - 17973 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
New York
Springer US
01.12.2022
Springer Nature B.V |
| Schlagworte: | |
| ISSN: | 0924-669X, 1573-7497 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Constrained Multiobjective Problem (CMOP) is widely used in engineering applications, but the current constrained Multiobjective Optimization algorithms (CMOEA) often fails to effectively balance convergence and diversity. For this purpose, a two-stage co-evolution constrained multi-objective optimization evolutionary algorithm (TSC-CMOEA) is presented to solve constrained multi-objective optimization problems. This method divides the search process into two phases: in the first stage, the synchronous co-evolution is used, and the population corresponding to the help problem and the population corresponding to the raw problem cooperate with each other and share the offspring to produce better solutions, so as to quickly cross the infeasible region and approach the Pareto front; The second stage discards the help problem when it fails and maintains only the evolution of the main population to save computing resources and enhance convergence. The combination of synchronous co-evolution and staged strategy allows the population to traverse infeasible regions more efficiently and converge quickly to feasible and non-dominant regions. The test results on benchmark CMOPs show that the convergence and population distribution of TSC-CMOEA is significantly better than those of NSGA-II, NSGA-III, C-MOEA/D, PPS, ToP and CCMO. |
|---|---|
| AbstractList | Constrained Multiobjective Problem (CMOP) is widely used in engineering applications, but the current constrained Multiobjective Optimization algorithms (CMOEA) often fails to effectively balance convergence and diversity. For this purpose, a two-stage co-evolution constrained multi-objective optimization evolutionary algorithm (TSC-CMOEA) is presented to solve constrained multi-objective optimization problems. This method divides the search process into two phases: in the first stage, the synchronous co-evolution is used, and the population corresponding to the help problem and the population corresponding to the raw problem cooperate with each other and share the offspring to produce better solutions, so as to quickly cross the infeasible region and approach the Pareto front; The second stage discards the help problem when it fails and maintains only the evolution of the main population to save computing resources and enhance convergence. The combination of synchronous co-evolution and staged strategy allows the population to traverse infeasible regions more efficiently and converge quickly to feasible and non-dominant regions. The test results on benchmark CMOPs show that the convergence and population distribution of TSC-CMOEA is significantly better than those of NSGA-II, NSGA-III, C-MOEA/D, PPS, ToP and CCMO. |
| Author | Cheng, Fanyong Zeng, Zhenhuan Fan, Chaodong Ai, Zhaoyang Wang, Jiawei Xiao, Leyi |
| Author_xml | – sequence: 1 givenname: Chaodong surname: Fan fullname: Fan, Chaodong organization: School of Computer Science, Xiangtan University, Foshan Green Intelligent Manufacturing Research Institute of Xiangtan University, Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment of Ministry of Education, Anhui Polytechnic University, School of Information Technology and Management, Hunan University of Finance and Economics – sequence: 2 givenname: Jiawei surname: Wang fullname: Wang, Jiawei organization: School of Computer Science, Xiangtan University, Foshan Green Intelligent Manufacturing Research Institute of Xiangtan University – sequence: 3 givenname: Leyi surname: Xiao fullname: Xiao, Leyi email: xiaolyttkx@163.com organization: Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment of Ministry of Education, Anhui Polytechnic University, School of Information Technology and Management, Hunan University of Finance and Economics, AnHui Key Laboratory of Detection Technology and Energy Saving Devices, AnHui Polytechnic University – sequence: 4 givenname: Fanyong surname: Cheng fullname: Cheng, Fanyong organization: Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment of Ministry of Education, Anhui Polytechnic University – sequence: 5 givenname: Zhaoyang surname: Ai fullname: Ai, Zhaoyang organization: College of Foreign Languages; Inter-disciplinary Research Center of Language Intelligence and Cultural Heritages, Hunan University – sequence: 6 givenname: Zhenhuan surname: Zeng fullname: Zeng, Zhenhuan organization: School of Computer Science, Xiangtan University |
| BookMark | eNp9kEtLxDAUhYOM4MzoH3BVcB3No9Mky2HwBYILXbiRkKZJ7dA2Y5KOzL83tYLgYsgi3Jvz3ZN7FmDWu94AcInRNUaI3QSMci4gIgQimhMM2QmY4xWjkOWCzcAcCZLDohBvZ2ARwhYhRCnCc_C-zrQze9cOsXF9ptra-SZ-dFmpgqmy1IpfDoao6lSF6FU09SGzziesH-umTw_d0MYGunJrdGz2Jtt5V7amC-fg1Ko2mIvfewle7m5fNw_w6fn-cbN-gppiEWGhOEO8rLClhCpFrOAqNxVeIcqU1QXhmlBcaEToeKziigq2IhXDlim6BFfT1GT7OZgQ5dYNvk-GkjDKOGIkF0nFJ5X2LgRvrNRNVOPW4xatxEiOUcopSpmilD9RSpZQ8g_d-aZT_nAcohMUkrivjf_71RHqG9b3iWs |
| CitedBy_id | crossref_primary_10_1016_j_engappai_2023_106004 crossref_primary_10_3390_math13091441 crossref_primary_10_1007_s40747_023_01181_6 crossref_primary_10_1007_s12293_024_00409_3 crossref_primary_10_1109_TEVC_2023_3345470 |
| Cites_doi | 10.1109/MCI.2017.2742868 10.1109/TEVC.2018.2855411 10.1016/j.asoc.2017.06.053 10.1109/TEVC.2019.2894743 10.1109/4235.996017 10.1109/TEVC.2020.2981949 10.1007/s00500-019-03794-x 10.1109/TEVC.2017.2690446 10.1007/s10845-020-01565-2 10.1109/TEVC.2019.2895860 10.1016/j.swevo.2011.02.002 10.1109/TEVC.2019.2896967 10.1109/TSG.2016.2598678 10.1016/j.knosys.2016.07.001 10.1016/j.asoc.2016.04.030 10.1016/j.asoc.2018.10.028 10.1142/S0218001421590321 10.1109/TEVC.2008.2009032 10.1109/CEC.2018.8477730 10.1016/j.swevo.2020.100799 10.1109/4235.873238 10.1007/s10845-017-1294-6 10.1016/j.knosys.2021.107693 10.1016/j.ejor.2017.03.048 10.1016/j.swevo.2018.08.017 10.1016/j.ins.2021.04.050 10.1109/TEVC.2013.2281535 10.1109/TCYB.2018.2819208 10.1109/CEC.2016.7743830 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 – notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. |
| DBID | AAYXX CITATION 3V. 7SC 7WY 7WZ 7XB 87Z 8AL 8FD 8FE 8FG 8FK 8FL ABJCF ABUWG AFKRA ARAPS AZQEC BENPR BEZIV BGLVJ CCPQU DWQXO FRNLG F~G GNUQQ HCIFZ JQ2 K60 K6~ K7- L.- L6V L7M L~C L~D M0C M0N M7S P5Z P62 PHGZM PHGZT PKEHL PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI PRINS PSYQQ PTHSS Q9U |
| DOI | 10.1007/s10489-022-03421-7 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts ABI/INFORM Collection ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ABI/INFORM Collection Computing Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials - QC ProQuest Central Business Premium Collection Technology Collection ProQuest One ProQuest Central Business Premium Collection (Alumni) ABI/INFORM Global (Corporate) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection Computer Science Database (ProQuest) ABI/INFORM Professional Advanced ProQuest Engineering Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ABI/INFORM Global Computing Database Engineering Database (Proquest) Advanced Technologies & Aerospace Collection ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Business ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest One Psychology Engineering Collection ProQuest Central Basic |
| DatabaseTitle | CrossRef ProQuest Business Collection (Alumni Edition) ProQuest One Psychology Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China ABI/INFORM Complete ProQuest One Applied & Life Sciences ProQuest Central (New) Engineering Collection Advanced Technologies & Aerospace Collection Business Premium Collection ABI/INFORM Global Engineering Database ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest Business Collection ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ABI/INFORM Global (Corporate) ProQuest One Business Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Central ABI/INFORM Professional Advanced ProQuest Engineering Collection ProQuest Central Korea Advanced Technologies Database with Aerospace ABI/INFORM Complete (Alumni Edition) ProQuest Computing ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database Materials Science & Engineering Collection ProQuest One Business (Alumni) ProQuest Central (Alumni) Business Premium Collection (Alumni) |
| DatabaseTitleList | ProQuest Business Collection (Alumni Edition) |
| Database_xml | – sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1573-7497 |
| EndPage | 17973 |
| ExternalDocumentID | 10_1007_s10489_022_03421_7 |
| GrantInformation_xml | – fundername: Open Fund Project of Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment of Ministry of Education grantid: No. GDSC202020 – fundername: Natural Science Foundation of Hunan Province grantid: No. 2020JJ4587 funderid: https://doi.org/10.13039/501100004735 – fundername: Open Fund Project of Fujian Provincial Key Laboratory of Data Intensive Computing grantid: No. BD202004 – fundername: Open Research Fund of AnHui Key Laboratory of Detection Technology and Energy Saving Devices grantid: No. JCKJ2021B05 – fundername: Guangdong Basic and Applied Basic Research Foundation grantid: No. 2019A1515110423 |
| GroupedDBID | -4Z -59 -5G -BR -EM -Y2 -~C -~X .86 .DC .VR 06D 0R~ 0VY 1N0 1SB 2.D 203 23M 28- 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 3V. 4.4 406 408 409 40D 40E 5GY 5QI 5VS 67Z 6NX 77K 7WY 8FE 8FG 8FL 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTV ABHLI ABHQN ABIVO ABJCF ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTAH ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. BA0 BBWZM BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DWQXO EBLON EBS EIOEI EJD ESBYG FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GROUPED_ABI_INFORM_COMPLETE GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K60 K6V K6~ K7- KDC KOV KOW L6V LAK LLZTM M0C M0N M4Y M7S MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P2P P62 P9O PF0 PQBIZ PQBZA PQQKQ PROAC PSYQQ PT4 PT5 PTHSS Q2X QOK QOS R4E R89 R9I RHV RNI RNS ROL RPX RSV RZC RZE RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TEORI TSG TSK TSV TUC U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z7R Z7X Z7Z Z81 Z83 Z88 Z8M Z8N Z8R Z8T Z8U Z8W Z92 ZMTXR ZY4 ~A9 ~EX 77I AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG ADKFA AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PQGLB 7SC 7XB 8AL 8FD 8FK JQ2 L.- L7M L~C L~D PKEHL PQEST PQUKI PRINS Q9U |
| ID | FETCH-LOGICAL-c319t-6a8708bd1f323aa2f98a4ed15037afc628c2316c0232323fa8a39752d71f7a3 |
| IEDL.DBID | K7- |
| ISICitedReferencesCount | 6 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000778916100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0924-669X |
| IngestDate | Wed Nov 05 14:54:08 EST 2025 Tue Nov 18 22:00:32 EST 2025 Sat Nov 29 05:33:29 EST 2025 Fri Feb 21 02:44:32 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 15 |
| Keywords | Constrained multi-objective evolutionary algorithms Coevolution Multiobjective optimization Constraint handling technique |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c319t-6a8708bd1f323aa2f98a4ed15037afc628c2316c0232323fa8a39752d71f7a3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 2737807249 |
| PQPubID | 326365 |
| PageCount | 20 |
| ParticipantIDs | proquest_journals_2737807249 crossref_citationtrail_10_1007_s10489_022_03421_7 crossref_primary_10_1007_s10489_022_03421_7 springer_journals_10_1007_s10489_022_03421_7 |
| PublicationCentury | 2000 |
| PublicationDate | 20221200 2022-12-00 20221201 |
| PublicationDateYYYYMMDD | 2022-12-01 |
| PublicationDate_xml | – month: 12 year: 2022 text: 20221200 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York – name: Boston |
| PublicationSubtitle | The International Journal of Research on Intelligent Systems for Real Life Complex Problems |
| PublicationTitle | Applied intelligence (Dordrecht, Netherlands) |
| PublicationTitleAbbrev | Appl Intell |
| PublicationYear | 2022 |
| Publisher | Springer US Springer Nature B.V |
| Publisher_xml | – name: Springer US – name: Springer Nature B.V |
| References | WoldesenbetYGYenGGTessemaBGConstraint handling in multiobjective evolutionary optimizationIEEE Trans Evol Comput200913351452510.1109/TEVC.2008.2009032 YiWGaoLPeiZLuJChenYε constrained differential evolution using halfspace partition for optimization problemsJ Intell Manuf202132115717810.1007/s10845-020-01565-2 Xia M, Dong M (2021) A novel two-archive evolutionary algorithm for constrained multi-objective optimization with small feasible regions. Knowl-Based Syst,, pp 107693 ZhuQZhangQLinQA constrained multiobjective evolutionary algorithm with detect-and-escape strategyIEEE Trans Evol Comput202024593894710.1109/TEVC.2020.2981949 WangJLiangGZhangJCooperative differential evolution framework for constrained multiobjective optimizationIEEE Trans Cybern20184962060207210.1109/TCYB.2018.2819208 LiKChenRFuGYaoXTwo-archive evolutionary algorithm for constrained multiobjective optimizationIEEE Trans Evol Comput201823230331510.1109/TEVC.2018.2855411 aJDbSGCDMAFHA practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithmsSwarm Evol Comput20111131810.1016/j.swevo.2011.02.002 GongMWangZZhuZJiaoLA similarity-based multiobjective evolutionary algorithm for deployment optimization of near space communication systemIEEE Trans Evol Comput201721687889710.1109/TEVC.2017.2690446 Zhou J, Zou J, Yang S, Zheng J, Pei T (2021) Niche-based and angle-based selection strategies for many-objective evolutionary optimization. Information Sciences PengXLiuKJinYA dynamic optimization approach to the design of cooperative co-evolutionary algorithmsKnowl-Based Syst201610917418610.1016/j.knosys.2016.07.001 RunarssonTPYaoXStochastic ranking for constrained evolutionary optimizationIEEE Trans Evol Comput20004328429410.1109/4235.873238 DebKPratapAAgarwalSMeyarivanTAMTA fast and elitist multiobjective genetic algorithm: Nsga-iiIEEE Trans Evol Comput20026218219710.1109/4235.996017 WangJLiYZhangQZhangZGaoSCooperative multiobjective evolutionary algorithm with propulsive population for constrained multiobjective optimizationIEEE Trans Syst Man Cybern: Syst2021PP99116 DebKJainHAn evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: Solving problems with box constraintsIEEE Trans Evol Comput201418457760110.1109/TEVC.2013.2281535 MaZWangYEvolutionary constrained multiobjective optimization: Test suite construction and performance comparisonsIEEE Trans Evol Comput2019236972986144142210.1109/TEVC.2019.2896967 TianYChengRZhangXJinYPlatemo: A matlab platform for evolutionary multi-objective optimization [educational forum]IEEE Comput Intell Mag2017124738710.1109/MCI.2017.2742868 GargHA hybrid pso-ga algorithm for constrained optimization problemsAppl Math Comput201627429230534331371410.90197 Tian Y, Zhang T, Xiao J, Zhang X, Jin Y (2020) A coevolutionary framework for constrained multi-objective optimization problems. IEEE Transactions on systems, man, and cybernetics: systems FarzinHFotuhi-FiruzabadMMoeini-AghtaieMA stochastic multi-objective framework for optimal scheduling of energy storage systems in microgridsIEEE Trans Smart Grid20168111712710.1109/TSG.2016.2598678 Wang B-C, Li H-X, Zhang Q, Wang Y (2018) Decomposition-based multiobjective optimization for constrained evolutionary optimization. IEEE Transactions on systems, man, and cybernetics: systems Fan Z, Li W, Cai X, Hu K, Lin H, Li H (2016) Angle-based constrained dominance principle in moea/d for constrained multi-objective optimization problems. In: 2016 IEEE Congress on evolutionary computation (CEC), IEEE, pp 460–467 LiuRLiJMuCJiaoLA coevolutionary technique based on multi-swarm particle swarm optimization for dynamic multi-objective optimizationEur J Oper Res2017261310281051364563010.1016/j.ejor.2017.03.0481403.90611 ZhangX-YGongY-JLinYZhangJKwongSZhangJDynamic cooperative coevolution for large scale optimizationIEEE Trans Evol Comput201923693594810.1109/TEVC.2019.2895860 FanZLiWCaiXHuangHFangYYouYMoJWeiCGoodmanEAn improved epsilon constraint-handling method in moea/d for cmops with large infeasible regionsSoft Comput20192323124911251010.1007/s00500-019-03794-x Tian Y, Xiang X, Zhang X, Cheng R, Jin Y (2018) Sampling reference points on the pareto fronts of benchmark multi-objective optimization problems. In: 2018 IEEE congress on evolutionary computation (CEC), pp 1–6 (2018). IEEE LiuZZWangBCTangKHandling constrained multiobjective optimization problems via bidirectional coevolutionIEEE Trans Cybern2021PP99114 PandaAPaniSA symbiotic organisms search algorithm with adaptive penalty function to solve multi-objective constrained optimization problemsAppl Soft Comput201646C34436010.1016/j.asoc.2016.04.030 MohamedAWA novel differential evolution algorithm for solving constrained engineering optimization problemsJ Intell Manuf201829365969210.1007/s10845-017-1294-6 YuKLiangJQuBYueCPurpose-directed two-phase multiobjective differential evolution for constrained multiobjective optimizationSwarm Evol Comput20216010079910.1016/j.swevo.2020.100799 QiaoJZhouHYangCYangSA decomposition-based multiobjective evolutionary algorithm with angle-based adaptive penaltyAppl Soft Comput20197419020510.1016/j.asoc.2018.10.028 Tan B, Ma H, Mei Y, Zhang M (2018) Evolutionary multi-objective optimization for web service location allocation problem. IEEE Trans Serv Comput PengCLiuHLGuFAn evolutionary algorithm with directed weights for constrained multi-objective optimizationAppl Soft Comput20176061362210.1016/j.asoc.2017.06.053https://doi.org/10.1016/j.asoc.2017.06.053, https://www.sciencedirect.com/science/article/pii/S1568494617303964 FanZLiWCaiXLiHWeiCZhangQDebKGoodmanEPush and pull search for solving constrained multi-objective optimization problemsSwarm Evol Comput20194466567910.1016/j.swevo.2018.08.017 Yang N, Liu HL (2021) Adaptively allocating constraint-handling techniques for constrained multi-objective optimization problems. Int J Pattern Recognit Artif Intell LiuZZWangYHandling constrained multiobjective optimization problems with constraints in both the decision and objective spacesIEEE Trans Evol Comput201923587088410.1109/TEVC.2019.2894743 YG Woldesenbet (3421_CR8) 2009; 13 R Liu (3421_CR21) 2017; 261 K Deb (3421_CR4) 2014; 18 AW Mohamed (3421_CR24) 2018; 29 M Gong (3421_CR3) 2017; 21 Z Fan (3421_CR9) 2019; 44 J Wang (3421_CR27) 2018; 49 X Peng (3421_CR22) 2016; 109 X-Y Zhang (3421_CR23) 2019; 23 3421_CR1 ZZ Liu (3421_CR30) 2019; 23 H Farzin (3421_CR2) 2016; 8 Z Fan (3421_CR33) 2019; 23 K Yu (3421_CR18) 2021; 60 ZZ Liu (3421_CR15) 2021; PP 3421_CR17 3421_CR16 3421_CR13 3421_CR35 3421_CR14 J Wang (3421_CR29) 2021; PP J Qiao (3421_CR7) 2019; 74 Y Tian (3421_CR31) 2017; 12 Z Ma (3421_CR32) 2019; 23 A Panda (3421_CR19) 2016; 46 K Deb (3421_CR11) 2002; 6 Q Zhu (3421_CR5) 2020; 24 K Li (3421_CR28) 2018; 23 C Peng (3421_CR20) 2017; 60 JD a (3421_CR34) 2011; 1 H Garg (3421_CR6) 2016; 274 3421_CR26 W Yi (3421_CR12) 2021; 32 TP Runarsson (3421_CR10) 2000; 4 3421_CR25 |
| References_xml | – reference: DebKPratapAAgarwalSMeyarivanTAMTA fast and elitist multiobjective genetic algorithm: Nsga-iiIEEE Trans Evol Comput20026218219710.1109/4235.996017 – reference: GongMWangZZhuZJiaoLA similarity-based multiobjective evolutionary algorithm for deployment optimization of near space communication systemIEEE Trans Evol Comput201721687889710.1109/TEVC.2017.2690446 – reference: Fan Z, Li W, Cai X, Hu K, Lin H, Li H (2016) Angle-based constrained dominance principle in moea/d for constrained multi-objective optimization problems. In: 2016 IEEE Congress on evolutionary computation (CEC), IEEE, pp 460–467 – reference: WoldesenbetYGYenGGTessemaBGConstraint handling in multiobjective evolutionary optimizationIEEE Trans Evol Comput200913351452510.1109/TEVC.2008.2009032 – reference: MohamedAWA novel differential evolution algorithm for solving constrained engineering optimization problemsJ Intell Manuf201829365969210.1007/s10845-017-1294-6 – reference: FanZLiWCaiXLiHWeiCZhangQDebKGoodmanEPush and pull search for solving constrained multi-objective optimization problemsSwarm Evol Comput20194466567910.1016/j.swevo.2018.08.017 – reference: PengXLiuKJinYA dynamic optimization approach to the design of cooperative co-evolutionary algorithmsKnowl-Based Syst201610917418610.1016/j.knosys.2016.07.001 – reference: aJDbSGCDMAFHA practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithmsSwarm Evol Comput20111131810.1016/j.swevo.2011.02.002 – reference: DebKJainHAn evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: Solving problems with box constraintsIEEE Trans Evol Comput201418457760110.1109/TEVC.2013.2281535 – reference: ZhuQZhangQLinQA constrained multiobjective evolutionary algorithm with detect-and-escape strategyIEEE Trans Evol Comput202024593894710.1109/TEVC.2020.2981949 – reference: PandaAPaniSA symbiotic organisms search algorithm with adaptive penalty function to solve multi-objective constrained optimization problemsAppl Soft Comput201646C34436010.1016/j.asoc.2016.04.030 – reference: Wang B-C, Li H-X, Zhang Q, Wang Y (2018) Decomposition-based multiobjective optimization for constrained evolutionary optimization. IEEE Transactions on systems, man, and cybernetics: systems – reference: PengCLiuHLGuFAn evolutionary algorithm with directed weights for constrained multi-objective optimizationAppl Soft Comput20176061362210.1016/j.asoc.2017.06.053https://doi.org/10.1016/j.asoc.2017.06.053, https://www.sciencedirect.com/science/article/pii/S1568494617303964 – reference: WangJLiYZhangQZhangZGaoSCooperative multiobjective evolutionary algorithm with propulsive population for constrained multiobjective optimizationIEEE Trans Syst Man Cybern: Syst2021PP99116 – reference: RunarssonTPYaoXStochastic ranking for constrained evolutionary optimizationIEEE Trans Evol Comput20004328429410.1109/4235.873238 – reference: Tan B, Ma H, Mei Y, Zhang M (2018) Evolutionary multi-objective optimization for web service location allocation problem. IEEE Trans Serv Comput – reference: WangJLiangGZhangJCooperative differential evolution framework for constrained multiobjective optimizationIEEE Trans Cybern20184962060207210.1109/TCYB.2018.2819208 – reference: Zhou J, Zou J, Yang S, Zheng J, Pei T (2021) Niche-based and angle-based selection strategies for many-objective evolutionary optimization. Information Sciences – reference: FarzinHFotuhi-FiruzabadMMoeini-AghtaieMA stochastic multi-objective framework for optimal scheduling of energy storage systems in microgridsIEEE Trans Smart Grid20168111712710.1109/TSG.2016.2598678 – reference: GargHA hybrid pso-ga algorithm for constrained optimization problemsAppl Math Comput201627429230534331371410.90197 – reference: Xia M, Dong M (2021) A novel two-archive evolutionary algorithm for constrained multi-objective optimization with small feasible regions. Knowl-Based Syst,, pp 107693 – reference: Yang N, Liu HL (2021) Adaptively allocating constraint-handling techniques for constrained multi-objective optimization problems. Int J Pattern Recognit Artif Intell – reference: LiuZZWangBCTangKHandling constrained multiobjective optimization problems via bidirectional coevolutionIEEE Trans Cybern2021PP99114 – reference: ZhangX-YGongY-JLinYZhangJKwongSZhangJDynamic cooperative coevolution for large scale optimizationIEEE Trans Evol Comput201923693594810.1109/TEVC.2019.2895860 – reference: Tian Y, Zhang T, Xiao J, Zhang X, Jin Y (2020) A coevolutionary framework for constrained multi-objective optimization problems. IEEE Transactions on systems, man, and cybernetics: systems – reference: YiWGaoLPeiZLuJChenYε constrained differential evolution using halfspace partition for optimization problemsJ Intell Manuf202132115717810.1007/s10845-020-01565-2 – reference: QiaoJZhouHYangCYangSA decomposition-based multiobjective evolutionary algorithm with angle-based adaptive penaltyAppl Soft Comput20197419020510.1016/j.asoc.2018.10.028 – reference: Tian Y, Xiang X, Zhang X, Cheng R, Jin Y (2018) Sampling reference points on the pareto fronts of benchmark multi-objective optimization problems. In: 2018 IEEE congress on evolutionary computation (CEC), pp 1–6 (2018). IEEE – reference: FanZLiWCaiXHuangHFangYYouYMoJWeiCGoodmanEAn improved epsilon constraint-handling method in moea/d for cmops with large infeasible regionsSoft Comput20192323124911251010.1007/s00500-019-03794-x – reference: TianYChengRZhangXJinYPlatemo: A matlab platform for evolutionary multi-objective optimization [educational forum]IEEE Comput Intell Mag2017124738710.1109/MCI.2017.2742868 – reference: LiuRLiJMuCJiaoLA coevolutionary technique based on multi-swarm particle swarm optimization for dynamic multi-objective optimizationEur J Oper Res2017261310281051364563010.1016/j.ejor.2017.03.0481403.90611 – reference: YuKLiangJQuBYueCPurpose-directed two-phase multiobjective differential evolution for constrained multiobjective optimizationSwarm Evol Comput20216010079910.1016/j.swevo.2020.100799 – reference: LiKChenRFuGYaoXTwo-archive evolutionary algorithm for constrained multiobjective optimizationIEEE Trans Evol Comput201823230331510.1109/TEVC.2018.2855411 – reference: MaZWangYEvolutionary constrained multiobjective optimization: Test suite construction and performance comparisonsIEEE Trans Evol Comput2019236972986144142210.1109/TEVC.2019.2896967 – reference: LiuZZWangYHandling constrained multiobjective optimization problems with constraints in both the decision and objective spacesIEEE Trans Evol Comput201923587088410.1109/TEVC.2019.2894743 – volume: 12 start-page: 73 issue: 4 year: 2017 ident: 3421_CR31 publication-title: IEEE Comput Intell Mag doi: 10.1109/MCI.2017.2742868 – volume: 23 start-page: 303 issue: 2 year: 2018 ident: 3421_CR28 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2018.2855411 – volume: 60 start-page: 613 year: 2017 ident: 3421_CR20 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2017.06.053 – volume: 23 start-page: 870 issue: 5 year: 2019 ident: 3421_CR30 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2019.2894743 – volume: 6 start-page: 182 issue: 2 year: 2002 ident: 3421_CR11 publication-title: IEEE Trans Evol Comput doi: 10.1109/4235.996017 – volume: 24 start-page: 938 issue: 5 year: 2020 ident: 3421_CR5 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2020.2981949 – volume: 23 start-page: 12491 issue: 23 year: 2019 ident: 3421_CR33 publication-title: Soft Comput doi: 10.1007/s00500-019-03794-x – volume: 21 start-page: 878 issue: 6 year: 2017 ident: 3421_CR3 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2017.2690446 – volume: 32 start-page: 157 issue: 1 year: 2021 ident: 3421_CR12 publication-title: J Intell Manuf doi: 10.1007/s10845-020-01565-2 – volume: 23 start-page: 935 issue: 6 year: 2019 ident: 3421_CR23 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2019.2895860 – volume: 1 start-page: 3 issue: 1 year: 2011 ident: 3421_CR34 publication-title: Swarm Evol Comput doi: 10.1016/j.swevo.2011.02.002 – volume: 23 start-page: 972 issue: 6 year: 2019 ident: 3421_CR32 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2019.2896967 – ident: 3421_CR1 – volume: 8 start-page: 117 issue: 1 year: 2016 ident: 3421_CR2 publication-title: IEEE Trans Smart Grid doi: 10.1109/TSG.2016.2598678 – volume: 109 start-page: 174 year: 2016 ident: 3421_CR22 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2016.07.001 – volume: 46 start-page: 344 issue: C year: 2016 ident: 3421_CR19 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2016.04.030 – volume: 274 start-page: 292 year: 2016 ident: 3421_CR6 publication-title: Appl Math Comput – volume: 74 start-page: 190 year: 2019 ident: 3421_CR7 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2018.10.028 – ident: 3421_CR16 – ident: 3421_CR26 doi: 10.1142/S0218001421590321 – volume: 13 start-page: 514 issue: 3 year: 2009 ident: 3421_CR8 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2008.2009032 – ident: 3421_CR35 doi: 10.1109/CEC.2018.8477730 – volume: 60 start-page: 100799 year: 2021 ident: 3421_CR18 publication-title: Swarm Evol Comput doi: 10.1016/j.swevo.2020.100799 – volume: 4 start-page: 284 issue: 3 year: 2000 ident: 3421_CR10 publication-title: IEEE Trans Evol Comput doi: 10.1109/4235.873238 – volume: 29 start-page: 659 issue: 3 year: 2018 ident: 3421_CR24 publication-title: J Intell Manuf doi: 10.1007/s10845-017-1294-6 – ident: 3421_CR14 doi: 10.1016/j.knosys.2021.107693 – volume: 261 start-page: 1028 issue: 3 year: 2017 ident: 3421_CR21 publication-title: Eur J Oper Res doi: 10.1016/j.ejor.2017.03.048 – volume: 44 start-page: 665 year: 2019 ident: 3421_CR9 publication-title: Swarm Evol Comput doi: 10.1016/j.swevo.2018.08.017 – volume: PP start-page: 1 issue: 99 year: 2021 ident: 3421_CR15 publication-title: IEEE Trans Cybern – ident: 3421_CR25 doi: 10.1016/j.ins.2021.04.050 – volume: PP start-page: 1 issue: 99 year: 2021 ident: 3421_CR29 publication-title: IEEE Trans Syst Man Cybern: Syst – volume: 18 start-page: 577 issue: 4 year: 2014 ident: 3421_CR4 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2013.2281535 – volume: 49 start-page: 2060 issue: 6 year: 2018 ident: 3421_CR27 publication-title: IEEE Trans Cybern doi: 10.1109/TCYB.2018.2819208 – ident: 3421_CR17 – ident: 3421_CR13 doi: 10.1109/CEC.2016.7743830 |
| SSID | ssj0003301 |
| Score | 2.3421915 |
| Snippet | Constrained Multiobjective Problem (CMOP) is widely used in engineering applications, but the current constrained Multiobjective Optimization algorithms... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 17954 |
| SubjectTerms | Artificial Intelligence Computer Science Convergence Evolutionary algorithms Machines Manufacturing Mechanical Engineering Multiple objective analysis Optimization Pareto optimization Population distribution Processes Search process |
| SummonAdditionalLinks | – databaseName: SpringerLINK Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3NS8MwFA86PXhxfuJ0Sg7eNNCmtUmPQxweZIgT2UVKmjRzMldZ68T_3pc03VRUUHpqm4TwXt57v0feB0LH4J95QtGUhIwKYkpoEZHqmEgwtylXSvnaJgpfsV6PDwbxtUsKK-po9_pK0mrqD8luoQnvAefJlK3zCVtGK2DuuBHHm_7dXP-Ch2775IFnQaIoHrhUme_X-GyOFhjzy7WotTbd5v_2uYHWHbrEneo4bKKlbLKFmnXnBuwEeRvdd7DMs5k7dliMh_l0VD48YWPUFIZP5WtOADgO4a2oCti-YcC3MM2UnBWATRW2wYgkTx8rpYldc5piB_W7F7fnl8Q1WiASJLAkkQCp5SnwJaCBEFTHXISZAqwYMKFlRLkEGBhJsO_m0YILgDFnVDFfMxHsosYkn2R7CCud-VlqdEKQhkwzDnjGi700oNqTVMoW8mtqJ9LVIDd7HieL6smGeglQL7HUS1gLncznPFcVOH4d3a6ZmDhpLBKAaIx7DDzNFjqtmbb4_fNq-38bfoDWqOG7jXZpo0Y5fckO0aqclaNiemRP6TvQ8-Fq priority: 102 providerName: Springer Nature |
| Title | A coevolution algorithm based on two-staged strategy for constrained multi-objective problems |
| URI | https://link.springer.com/article/10.1007/s10489-022-03421-7 https://www.proquest.com/docview/2737807249 |
| Volume | 52 |
| WOSCitedRecordID | wos000778916100001&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: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1573-7497 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0003301 issn: 0924-669X databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3dS8MwED_8evDF-YnTOfLgmwbbdDbtk8yhCOoYm-gUpKRJMxVd1VbF_95Ll24o6IsUDvqREHqXu98llzuAbfTPHKFYTBucCWpSaFER65BKNLdxoJRydXFQ-Iy320G_H3bsgltmwypLnVgoapVKs0a-h2aWBw5Hb-Hg-YWaqlFmd9WW0JiGWZcx18j5KadjTYy-elExD30M6vth3x6asUfnGiZYCF0xkwTPpfy7YZqgzR8bpIXdOa78d8SLsGARJ2mORGQJppLhMlTKag7ETu4VuG0SmSbvVhSJeBxgZ_ndEzGGThF8lH-kFMHkAO-yUVLbT4KYF5uZNLQC8aoiRYAiTeOHkSIltmBNtgq946OL1gm1xReoxFmZU1_gTA5i5JXHPCGYDgPRSBTiR48LLX0WSISGvkSbby4tAoHQZp8p7mouvDWYGabDZB2I0ombxEZPeHGDax4gxnFCJ_aYdiSTsgpu-d8jafOSmzE_RpOMyoZXEfIqKngV8SrsjNs8j7Jy_Pl1rWRQZGdoFk24U4XdksWT17_3tvF3b5swz4xUFREvNZjJX9-SLZiT7_l99lqHaX51XYfZw6N2p1svpBXpudMylPeQdvZvkHZ7l1_dLPHR |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1JS8QwFH7oKOjFXRzXHPSkwTatTXsQERcUx0HQw1y0pEnjgk5HWxX_kz_SlzZ1UNCbB-mpS0LafHnfe81bAFbRPnOEYgn1ORPUpNCiItERlUi3SaiUcnUZKNzi7XbY6URnA_Bex8IYt8paJpaCWmXS_CPfRJrlocPRWtjpPVJTNcrsrtYlNCpYnKRvr2iy5dvH-zi_a4wdHlzsHVFbVYBKhFtBA4EQDRMchMc8IZiOQuGnChUjjwstAxZK1HkCiWRmDi1CgZy9xRR3NRce9joIQ76Pi8E4Cjp7n3Lf88piyw5aNDQIoo4N0bGBer5xTULDz6Tccyn_SoN93fbbdmzJcofj_-v7TMCY1abJbgX_SRhIu1MwXleqIFZwTcPlLpFZ-mKXGRH31zj04uaBGBJXBC8VrxlFRfkaz_IqYe8bQX0em5kUuwJ1cUVK50uaJXcVSRBbjCefgfM_eMVZaHSzbjoHROnUTRMjA73E55qHqL85kZN4TDuSSdkEt57lWNqc62bM93E_W7RBRozIiEtkxLwJ659telXGkV-fXqzhEFvpk8d9LDRhowZU__bPvc3_3tsKjBxdnLbi1nH7ZAFGmcFz6dmzCI3i6TldgmH5UtzmT8vlyiBw9bdA-wCEQUTm |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LS8QwEB58IV5cn7g-c9CTBtt0bdqDiLguysoi6GEvWtKk8YFu1VZl_5k_z0mb7qKgNw_SUx8JafPNfDPNZAZgE_0zRygW0wZngpoUWlTEOqQS6TYOlFKuLjYKn_FOJ-h2w_MR-Kj2wpiwykonFopapdL8I99FmuWBw9Fb2NU2LOK82Tp4eqamgpRZaa3KaZQQaSf9d3Tfsv3TJs71FmOt48ujE2orDFCJ0MupLxCuQYwD8pgnBNNhIBqJQiPJ40JLnwUS7R9fIrGZQ4tAIH_vMcVdzYWHvY7COHLwnpGwNqcDDvC8ovCyg94N9f2wa7fr2E17DROmhE6gSb_nUv6VEod27rel2YLxWrX_-61mYNpa2eSwFItZGEl6c1CrKlgQq9Dm4eqQyDR5s-JHxMMNDj2_fSSG3BXBS_l7StGAvsGzrEzk2ydo52Mzk3pXoI2uSBGUSdP4viQPYov0ZAtw8QevuAhjvbSXLAFROnGT2OhGL25wzQO065zQiT2mHcmkrINbzXgkbS52M-aHaJhF2qAkQpREBUoiXoftQZunMhPJr0-vVtCIrFbKoiEu6rBTgWt4--feln_vbQMmEV_R2WmnvQJTzEC7CPhZhbH85TVZgwn5lt9lL-uFkBC4_lucfQJtSE2M |
| 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=A+coevolution+algorithm+based+on+two-staged+strategy+for+constrained+multi-objective+problems&rft.jtitle=Applied+intelligence+%28Dordrecht%2C+Netherlands%29&rft.au=Fan%2C+Chaodong&rft.au=Wang%2C+Jiawei&rft.au=Xiao%2C+Leyi&rft.au=Cheng%2C+Fanyong&rft.date=2022-12-01&rft.issn=0924-669X&rft.eissn=1573-7497&rft.volume=52&rft.issue=15&rft.spage=17954&rft.epage=17973&rft_id=info:doi/10.1007%2Fs10489-022-03421-7&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s10489_022_03421_7 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0924-669X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0924-669X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0924-669X&client=summon |