Evolutionary Large-Scale Multi-Objective Optimization: A Survey
Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in solving various optimization problems, but their performance may deteriorate drastically when tackling problems containing a large number of decision variables. In recent years, much effort been devoted to addressing...
Saved in:
| Published in | ACM computing surveys Vol. 54; no. 8; pp. 1 - 34 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York, NY
ACM
30.11.2022
Association for Computing Machinery |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0360-0300 1557-7341 |
| DOI | 10.1145/3470971 |
Cover
Loading…
| Abstract | Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in solving various optimization problems, but their performance may deteriorate drastically when tackling problems containing a large number of decision variables. In recent years, much effort been devoted to addressing the challenges brought by large-scale multi-objective optimization problems. This article presents a comprehensive survey of stat-of-the-art MOEAs for solving large-scale multi-objective optimization problems. We start with a categorization of these MOEAs into decision variable grouping based, decision space reduction based, and novel search strategy based MOEAs, discussing their strengths and weaknesses. Then, we review the benchmark problems for performance assessment and a few important and emerging applications of MOEAs for large-scale multi-objective optimization. Last, we discuss some remaining challenges and future research directions of evolutionary large-scale multi-objective optimization. |
|---|---|
| AbstractList | Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in solving various optimization problems, but their performance may deteriorate drastically when tackling problems containing a large number of decision variables. In recent years, much effort been devoted to addressing the challenges brought by large-scale multi-objective optimization problems. This article presents a comprehensive survey of stat-of-the-art MOEAs for solving large-scale multi-objective optimization problems. We start with a categorization of these MOEAs into decision variable grouping based, decision space reduction based, and novel search strategy based MOEAs, discussing their strengths and weaknesses. Then, we review the benchmark problems for performance assessment and a few important and emerging applications of MOEAs for large-scale multi-objective optimization. Last, we discuss some remaining challenges and future research directions of evolutionary large-scale multi-objective optimization. |
| ArticleNumber | 174 |
| Author | Zhang, Xingyi Tan, Kay Chen Jin, Yaochu Cheng, Ran Tian, Ye He, Cheng Si, Langchun |
| Author_xml | – sequence: 1 givenname: Ye surname: Tian fullname: Tian, Ye email: field910921@gmail.com organization: Anhui University, Hefei, China – sequence: 2 givenname: Langchun surname: Si fullname: Si, Langchun email: spring_slc@163.com organization: Anhui University, Hefei, China – sequence: 3 givenname: Xingyi surname: Zhang fullname: Zhang, Xingyi email: xyzhanghust@gmail.com organization: Anhui University, Hefei, China – sequence: 4 givenname: Ran surname: Cheng fullname: Cheng, Ran email: ranchengcn@gmail.com organization: Southern University of Science and Technology, Shenzhen, China – sequence: 5 givenname: Cheng surname: He fullname: He, Cheng email: chenghehust@gmail.com organization: Southern University of Science and Technology, Shenzhen, China – sequence: 6 givenname: Kay Chen surname: Tan fullname: Tan, Kay Chen email: kaychen.tan@polyu.edu organization: The Hong Kong Polytechnic University, Hong Kong SAR – sequence: 7 givenname: Yaochu surname: Jin fullname: Jin, Yaochu email: yaochu.jin@surrey.ac.uk organization: University of Surrey, Guildford, U.K |
| BookMark | eNpt0DtPwzAQB3ALFYm2IHamSAxMBtvnR8KCqqo8pKIO7R455opcpUlxnErl05PSwoCYbrif_vcYkF5VV0jIJWe3nEt1B9KwzPAT0udKGWpA8h7pM9CMMmDsjAyaZsUYE5LrPnmYbOuyjb6ubNglUxvekc6dLTF5bcvo6axYoYt-i8lsE_3af9q9vU9GybwNW9ydk9OlLRu8ONYhWTxOFuNnOp09vYxHU2oB0kil1gXwJaSZ4hplYVEol0kOoA1XTigrmcRCv2VGAZMy7fZDYZjGNNNOwZBcH2I3of5osYn5qm5D1U3MheEGIBNadOrmoFyomybgMt8Ev-4OyznL98_Jj8_pJP0jnY_fp8VgffmPvzp469a_oT_NL7zRbEU |
| CitedBy_id | crossref_primary_10_1007_s40747_023_01262_6 crossref_primary_10_1109_JAS_2022_105437 crossref_primary_10_1109_TVCG_2024_3456142 crossref_primary_10_1038_s41598_025_86298_z crossref_primary_10_1109_TGRS_2025_3540269 crossref_primary_10_1109_TCYB_2023_3287596 crossref_primary_10_1016_j_ins_2023_119003 crossref_primary_10_1109_TEVC_2022_3199775 crossref_primary_10_1007_s44196_023_00336_0 crossref_primary_10_1109_ACCESS_2025_3543741 crossref_primary_10_1109_TEVC_2022_3213006 crossref_primary_10_1109_TETCI_2023_3300526 crossref_primary_10_1109_TEVC_2023_3349073 crossref_primary_10_1016_j_jhazmat_2025_137572 crossref_primary_10_1109_JSEN_2024_3409459 crossref_primary_10_1109_TSMC_2024_3418346 crossref_primary_10_1007_s10489_023_04500_z crossref_primary_10_1109_MCI_2024_3363980 crossref_primary_10_1109_TETCI_2022_3146882 crossref_primary_10_1109_TETCI_2024_3353590 crossref_primary_10_1109_TGRS_2024_3472749 crossref_primary_10_1109_MCI_2024_3487134 crossref_primary_10_1109_TCYB_2022_3178929 crossref_primary_10_1109_TEVC_2021_3111209 crossref_primary_10_1109_TAI_2024_3414289 crossref_primary_10_1109_JAS_2024_124548 crossref_primary_10_1109_TNNLS_2024_3371706 crossref_primary_10_34133_icomputing_0025 crossref_primary_10_1299_jamdsm_2024jamdsm0079 crossref_primary_10_1007_s12293_022_00358_9 crossref_primary_10_1021_acs_jcim_4c00031 crossref_primary_10_1007_s10586_024_04862_0 crossref_primary_10_1016_j_ins_2023_119856 crossref_primary_10_1109_TETCI_2022_3145706 crossref_primary_10_1371_journal_pone_0301630 crossref_primary_10_1080_00207543_2023_2267680 crossref_primary_10_1109_TCYB_2022_3226744 crossref_primary_10_1049_rsn2_12347 crossref_primary_10_1109_TCYB_2023_3265652 crossref_primary_10_1109_TEVC_2023_3259339 crossref_primary_10_1007_s41965_024_00172_x crossref_primary_10_3389_fenrg_2022_988772 crossref_primary_10_1109_TNSM_2023_3332356 crossref_primary_10_1016_j_asoc_2023_110295 crossref_primary_10_1145_3617380 crossref_primary_10_1109_TCE_2024_3439711 crossref_primary_10_1007_s40747_021_00553_0 crossref_primary_10_1109_TEVC_2023_3256183 crossref_primary_10_1109_TIM_2024_3440371 crossref_primary_10_2478_fcds_2024_0001 crossref_primary_10_1155_2024_9143774 crossref_primary_10_1109_TRO_2024_3463476 crossref_primary_10_1007_s11356_023_30423_w crossref_primary_10_34133_research_0442 crossref_primary_10_1007_s10489_022_04291_9 crossref_primary_10_1016_j_ins_2024_121347 crossref_primary_10_1007_s10489_023_04574_9 crossref_primary_10_1109_TEVC_2022_3155533 crossref_primary_10_1109_TAI_2022_3168038 crossref_primary_10_1109_JAS_2022_105875 crossref_primary_10_1109_TETCI_2023_3330513 crossref_primary_10_1109_TSMC_2023_3299570 crossref_primary_10_1002_spy2_469 crossref_primary_10_1109_TETCI_2024_3372378 crossref_primary_10_1007_s40747_022_00963_8 crossref_primary_10_1007_s40747_024_01616_8 crossref_primary_10_1109_TCYB_2022_3225341 crossref_primary_10_1109_TVCG_2023_3326921 crossref_primary_10_1109_TEVC_2023_3250350 crossref_primary_10_1016_j_ins_2023_119946 crossref_primary_10_3389_fnhum_2024_1400077 crossref_primary_10_1109_TSMC_2022_3186546 crossref_primary_10_2478_jee_2024_0018 crossref_primary_10_1109_TETCI_2024_3404020 crossref_primary_10_1155_2024_6852701 crossref_primary_10_1109_TEVC_2024_3355221 crossref_primary_10_1109_TSMC_2024_3446822 crossref_primary_10_1007_s10973_024_13023_9 crossref_primary_10_1109_TEVC_2022_3144675 crossref_primary_10_1021_acs_jctc_3c01009 crossref_primary_10_1007_s10462_023_10522_3 crossref_primary_10_1109_TGRS_2022_3198426 crossref_primary_10_1007_s10836_021_05974_w crossref_primary_10_1109_TETCI_2024_3386866 crossref_primary_10_1016_j_ejrh_2022_101000 crossref_primary_10_1109_ACCESS_2022_3210254 crossref_primary_10_1109_TEVC_2022_3155593 crossref_primary_10_1007_s11269_023_03580_3 crossref_primary_10_1007_s10586_024_04600_6 crossref_primary_10_1109_ACCESS_2025_3541271 crossref_primary_10_1002_clen_202300471 |
| Cites_doi | 10.1016/j.ins.2020.02.066 10.1109/TCYB.2014.2322602 10.1145/3321707.3321729 10.1109/TEVC.2016.2600642 10.1145/3376916 10.1016/j.knosys.2015.12.022 10.1145/2792984 10.1002/int.4550080406 10.1109/TCYB.2018.2889413 10.1016/j.cie.2014.08.004 10.1109/TEVC.2018.2808689 10.1109/TEVC.2018.2879406 10.1109/SSCI.2016.7850214 10.1109/TEVC.2005.861417 10.1109/TEVC.2019.2950935 10.1109/TEVC.2018.2881153 10.1109/TSMCB.2012.2227469 10.1504/IJBIC.2016.076329 10.1109/TCYB.2020.2985081 10.1109/TCYB.2020.2979930 10.1016/j.asoc.2019.105991 10.1016/j.advengsoft.2016.01.008 10.1109/TEVC.2020.3004012 10.5555/129194 10.1109/TFUZZ.2019.2945241 10.1016/j.petrol.2020.107192 10.1023/A:1006529012972 10.1016/j.swevo.2011.03.001 10.1155/2020/3106097 10.1145/2001576.2001781 10.1007/978-3-030-12598-1_32 10.1016/j.asoc.2010.11.007 10.1109/TEVC.2017.2726341 10.1016/j.knosys.2016.05.033 10.1145/3205651.3208250 10.1109/TEVC.2013.2281543 10.1016/j.cor.2013.11.014 10.1007/978-3-642-33275-3_2 10.1109/TNNLS.2021.3061630 10.1109/TEVC.2018.2855411 10.1109/TBDATA.2020.2993446 10.1109/TEVC.2007.894202 10.1016/j.ins.2015.06.044 10.1109/TEVC.2018.2882166 10.1007/978-3-642-25832-9_20 10.1109/TCYB.2018.2821180 10.1038/s41598-019-45814-8 10.1109/TEVC.2012.2227145 10.1109/CEC.2017.7969315 10.1109/SSCI.2017.8280974 10.1145/2939672.2939861 10.1109/TGRS.2009.2023666 10.1007/978-3-319-54157-0_4 10.1109/TNNLS.2017.2695223 10.1007/s00500-017-2965-0 10.1016/j.ins.2018.10.005 10.1109/MCI.2017.2708578 10.1145/1390156.1390294 10.1109/TCYB.2015.2409837 10.1109/CEC.2013.6557555 10.1109/TEVC.2013.2281535 10.1109/TCYB.2019.2906383 10.1109/TCYB.2016.2600577 10.1109/TII.2019.2962137 10.1109/TEVC.2014.2350995 10.1016/j.asoc.2014.08.036 10.1109/4235.996017 10.1109/TII.2018.2836189 10.1016/j.cosrev.2018.02.002 10.1109/TCYB.2018.2871673 10.1109/TEVC.2015.2455812 10.1109/TEVC.2017.2672689 10.1109/TEVC.2013.2260862 10.1109/CEC.2016.7743831 10.1109/TNNLS.2017.2677973 10.1007/s10852-007-9073-6 10.2307/1910081 10.1109/CEC.2017.7969486 10.1109/TEVC.2005.846356 10.1109/ACCESS.2020.2980942 10.1007/s10732-009-9103-9 10.1109/TEVC.2017.2782571 10.1016/j.ejor.2013.09.008 10.1038/s41598-017-12773-x 10.1214/aoms/1177729586 10.1023/A:1008202821328 10.1109/TEVC.2007.892759 10.1145/3319619.3322068 10.5555/1121732 10.1016/j.asoc.2017.09.033 10.1007/s13042-019-01030-4 10.1016/j.eswa.2018.03.018 10.1109/CEC48606.2020.9185553 10.1109/TSMC.2020.3003926 10.1038/s41598-017-00090-2 10.15837/ijccc.2011.2.2172 10.1109/TEVC.2006.877146 10.1145/3078848 10.1016/j.eswa.2020.113278 10.1109/TEVC.2017.2688863 10.1016/j.engappai.2018.09.009 10.5555/3298239.3298367 10.1109/TEVC.2017.2754271 10.1109/MCI.2017.2742868 10.1007/s10852-008-9080-2 10.1016/j.ejor.2008.01.054 10.1109/TEVC.2019.2918140 10.1016/j.swevo.2017.08.001 10.1016/j.asoc.2020.106120 10.1016/j.eswa.2004.10.014 10.1109/TEVC.2020.2987804 10.1186/s12859-017-1657-1 10.1145/3205455.3205491 10.1109/TEVC.2005.851275 10.1016/j.ins.2008.02.017 10.1109/TETCI.2018.2872055 10.1109/TEVC.2019.2909744 10.1109/TEVC.2019.2913831 10.1016/j.asoc.2018.07.014 10.1162/106365601750190398 10.1016/j.ins.2018.10.007 10.1016/j.swevo.2018.02.003 10.1016/j.neucom.2018.07.060 10.1016/j.swevo.2020.100684 10.1016/j.ejor.2007.05.055 10.1109/TEVC.2011.2161090 10.1109/TEVC.2005.859463 10.1109/TNSE.2020.2972980 10.21437/Interspeech.2019-2420 10.1109/5.784219 10.1109/TEVC.2018.2836912 10.1016/j.cor.2015.04.009 10.1109/TEVC.2021.3063606 10.1145/2517649 10.1155/2018/2613739 10.1109/TEVC.2015.2395073 10.1073/pnas.122653799 10.1109/TEVC.2017.2704782 10.1145/3176644 10.5555/645826.669582 10.1016/j.eswa.2016.06.005 10.1016/j.swevo.2019.100626 10.1016/j.ejor.2006.06.042 10.1145/2666003 10.1007/s10462-019-09800-w 10.1007/s10732-015-9289-y 10.5555/2888116.2888125 10.5555/2873822.2873894 10.1145/956750.956769 10.1109/TCYB.2017.2711038 10.1145/3205651.3208243 10.5555/3305381.3305429 10.1145/3321707.3321735 10.1109/TEVC.2020.3016049 10.1016/j.asoc.2014.08.024 10.1109/TCYB.2015.2507366 10.1109/TPWRS.2016.2620990 10.1109/TEVC.2020.2967501 10.5555/1887255.1887289 10.1145/3136625 10.1016/j.knosys.2015.07.006 10.1109/TNNLS.2015.2469673 10.1109/TEVC.2020.3044711 10.1109/TEVC.2019.2890858 10.1109/ICDCS.2018.00031 10.1007/s00500-018-3269-8 10.1109/TII.2017.2676000 10.1109/TEVC.2019.2896002 10.1145/331499.331504 10.1109/TCYB.2017.2720180 10.1016/j.neucom.2020.02.028 10.1109/TCYB.2020.2977661 10.1016/j.ejor.2006.08.008 10.5555/2969033.2969125 10.1145/2396761.2396775 10.1137/1.9781611972795.100 10.1109/TSMC.2018.2861879 10.1016/j.neucom.2020.01.114 10.1109/TSMCC.2008.919172 10.1162/106365600568202 10.1145/2908961.2908979 10.1109/CEC.2005.1554717 10.1007/s10589-005-3070-3 10.1109/MCDM.2009.4938830 |
| ContentType | Journal Article |
| Copyright | Association for Computing Machinery. Copyright Association for Computing Machinery Nov 2022 |
| Copyright_xml | – notice: Association for Computing Machinery. – notice: Copyright Association for Computing Machinery Nov 2022 |
| DBID | AAYXX CITATION 7SC 8FD JQ2 L7M L~C L~D |
| DOI | 10.1145/3470971 |
| 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 CrossRef |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1557-7341 |
| EndPage | 34 |
| ExternalDocumentID | 10_1145_3470971 3470971 |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: 61822301, 61876123, 61906001, 61906081, 61903178, and U20A20306 – fundername: Royal Society International Exchanges Program grantid: IEC\NSFC\170279 – fundername: Research Grants Council of the Hong Kong Special Administrative Region grantid: PolyU11202418 and PolyU11209219 – fundername: Hong Kong Scholars Program grantid: XJ2019035 – fundername: National Key R&D Program of China grantid: 2018AAA0100100 – fundername: Collaborative Innovation Program of Anhui grantid: GXXT-2020-051 – fundername: Anhui Provincial Natural Science Foundation grantid: 1908085QF271 |
| GroupedDBID | --Z -DZ -~X .4S .DC 23M 4.4 5GY 5VS 6J9 85S 8US 8VB AAIKC AAKMM AALFJ AAMNW AAYFX ABPPZ ACGFO ACGOD ACM ACNCT ADBCU ADL ADMLS ADPZR AEBYY AEGXH AEMOZ AENEX AENSD AFWIH AFWXC AGHSJ AHQJS AIAGR AIKLT AKVCP ALMA_UNASSIGNED_HOLDINGS ARCSS ASPBG AVWKF BDXCO CCLIF CS3 EBE EBR EBU EDO EMK FEDTE GUFHI HGAVV H~9 IAO ICD IEA IGS IOF K1G LHSKQ N95 P1C P2P PQQKQ QWB RNS ROL RXW TAE TH9 U5U UKR UPT VQA W7O WH7 X6Y XH6 XSW XZL YXB Z5M ZCA ZL0 AAYXX AEFXT AEJOY AETEA AKRVB CITATION 7SC 8FD JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-a338t-466b31f389516e4bae25c941336715c25a404eb6d97530448002e2706e896c53 |
| ISSN | 0360-0300 |
| IngestDate | Mon Jun 30 04:47:20 EDT 2025 Thu Apr 24 23:07:05 EDT 2025 Thu Jul 03 08:17:55 EDT 2025 Fri Feb 21 04:13:12 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 8 |
| Keywords | large-scale optimization evolutionary computation Multi-objective optimization |
| Language | English |
| License | Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-a338t-466b31f389516e4bae25c941336715c25a404eb6d97530448002e2706e896c53 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 2717339262 |
| PQPubID | 47570 |
| PageCount | 34 |
| ParticipantIDs | proquest_journals_2717339262 crossref_primary_10_1145_3470971 crossref_citationtrail_10_1145_3470971 acm_primary_3470971 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-11-30 |
| PublicationDateYYYYMMDD | 2022-11-30 |
| PublicationDate_xml | – month: 11 year: 2022 text: 2022-11-30 day: 30 |
| PublicationDecade | 2020 |
| PublicationPlace | New York, NY |
| PublicationPlace_xml | – name: New York, NY – name: Baltimore |
| PublicationTitle | ACM computing surveys |
| PublicationTitleAbbrev | ACM CSUR |
| PublicationYear | 2022 |
| Publisher | ACM Association for Computing Machinery |
| Publisher_xml | – name: ACM – name: Association for Computing Machinery |
| References | Lei Zhang, Shangshang Yang, Xinpeng Wu, Fan Cheng, Ying Xie, and Zhiting Lin. 2019. An indexed set representation based multi-objective evolutionary approach for mining diversified top-k high utility patterns. Engineering Applications of Artificial Intelligence 77 (2019), 9–20. Lei Zhang, Jiajun Xia, Fan Cheng, Jianfeng Qiu, and Xingyi Zhang. 2020. Multi-objective optimization of critical node detection based on cascade model in complex networks. IEEE Transactions on Network Science and Engineering 7, 3 (2020), 2052–2066. P. Shahsamandi Esfahani and A. Saghaei. 2017. A multi-objective approach to fuzzy clustering using ITLBO algorithm. Journal of AI and Data Mining 5, 2 (2017), 307–317. Urvesh Bhowan, Mark Johnston, and Mengjie Zhang. Ensemble learning and pruning in multi-objective genetic programming for classification with unbalanced data. In Proceedings of the 24th Australasian Joint Conference on Artificial Intelligence. 10.1007/978-3-642-25832-9_20 Seyedali Mirjalili and Andrew Lewis. 2016. The whale optimization algorithm. Advances in Engineering Software 95 (2016), 51–67. 10.1016/j.advengsoft.2016.01.008 Vivek K. Patel and Vimal J. Savsani. 2015. Heat transfer search (HTS): A novel optimization algorithm. Information Sciences 324 (2015), 217–246. 10.1016/j.ins.2015.06.044 D. Lin, S. Wang, and H. Yan. A multiobjective genetic algorithm for portfolio selection. In Proceedings of the 5th International Conference on Optimization: Techniques and Applications. Zhichao Lu, Ian Whalen, Vishnu Boddeti, Yashesh Dhebar, Kalyanmoy Deb, Erik Goodman, and Wolfgang Banzhaf. NSGA-Net: Neural architecture search using multiobjective genetic algorithm. In Proceedings of the 2019 Annual Genetic and Evolutionary Computation Conference. ACM, New York, NY. 10.1145/3321707.3321729 Mario Ventresca, Kyle Robert Harrison, and Beatrice M. Ombuki-Berman. An experimental evaluation of multi-objective evolutionary algorithms for detecting critical nodes in complex networks. In Proceedings of the 2015 European Conference on the Applications of Evolutionary Computation. Zhun Fan, Wenji Li, Xinye Cai, Li Hui, and Erik D. Goodman. 2019. Push and pull search for solving constrained multi-objective optimization problems. Swarm and Evolutionary Computation44 (2019), 665–679. Indrajit Saha, Ujjwal Maulik, and Dariusz Plewczynski. 2011. A new multi-objective technique for differential fuzzy clustering. Applied Soft Computing 11, 2 (2011), 2765–2776. 10.1016/j.asoc.2010.11.007 An Song, Qiang Yang, Wei-Neng Chen, and Jun Zhang. 2016. A random-based dynamic grouping strategy for large scale multi-objective optimization. In Proceedings of the 2016 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA, 468–475. Ye Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin. 2017. PlatEMO: A MATLAB platform for evolutionary multi-objective optimization [educational forum]. IEEE Computational Intelligence Magazine 12, 4 (2017), 73–87. Heiner Zille, Hisao Ishibuchi, Sanaz Mostaghim, and Yusuke Nojima. 2018. A framework for large-scale multiobjective optimization based on problem transformation. IEEE Transactions on Evolutionary Computation 22, 2 (2018), 260–275. Hui Li, Qingfu Zhang, Jingda Deng, and Zong-Ben Xu. 2018. A preference-based multiobjective evolutionary approach for sparse optimization. IEEE Transactions on Neural Networks and Learning Systems 29, 5 (2018), 1716–1731. Jun-Rong Jian, Zhi-Hui Zhan, and Jun Zhang. 2020. Large-scale evolutionary optimization: A survey and experimental comparative study. International Journal of Machine Learning and Cybernetics 11 (2020), 729–745. Seyedali Mirjalili. 2015. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems 89 (2015), 228–249. 10.1016/j.knosys.2015.07.006 Heiner Zille and Sanaz Mostaghim. Comparison study of large-scale optimisation techniques on the LSMOP benchmark functions. In Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence. Chengtao Li, David Alvarez-Melis, Keyulu Xu, Stefanie Jegelka, and Suvrit Sra. 2017. Distributional adversarial networks. arXiv:1706.09549. Karam M. Sallam, Saber M. Elsayed, Ripon K. Chakrabortty, and Michael J. Ryan. 2020. Improved multi-operator differential evolution algorithm for solving unconstrained problems. In Proceedings of the IEEE Congress on Evolutionary Computation. Imen Harbaoui Dridi, Ryan Kammarti, Mekki Ksouri, and Pierre Borne. 2011. Multi-objective optimization for the m-PDPTW: Aggregation method with use of genetic algorithm and lower bounds. International Journal of Computers, Communications & Control 6, 2 (2011), 246–257. Tinkle Chugh, Karthik Sindhya, Kaisa Miettinen, Yaochu Jin, Tomas Kratky, and Pekka Makkonen. 2017. Surrogate-assisted evolutionary multiobjective shape optimization of an air intake ventilation system. In Proceedings of the 2017 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA. Kalyanmoy Deb and Himanshu Jain. 2013. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation 18, 4 (2013), 577–601. M. T. M. Emmerich, K. C. Giannakoglou, and B. Naujoks. 2006. Single- and multi-objective evolutionary optimization assisted by Gaussian random field metamodels. IEEE Transactions on Evolutionary Computation 10, 4 (2006), 421–439. 10.1109/TEVC.2005.859463 Yutao Qi, Zhanting Hou, He Li, Jianbin Huang, and Xiaodong Li. 2015. A decomposition based memetic algorithm for multi-objective vehicle routing problem with time windows. Computers & Operations Research 62 (2015), 61–77. 10.1016/j.cor.2015.04.009 Yosef Masoudi-Sobhanzadeh, Yadollah Omidi, Massoud Amanlou, and Ali Masoudi-Nejad. 2019. Trader as a new optimization algorithm predicts drug-target interactions efficiently. Scientific Reports 9 (2019), 9348. Chao Qian, Yang Yu, and Zhihua Zhou. Pareto ensemble pruning. In Proceedings of the 29th AAAI Conference on Artificial Intelligence. 10.5555/2888116.2888125 Heiner Zille, Hisao Ishibuchi, Sanaz Mostaghim, and Yusuke Nojima. 2016. Weighted optimization framework for large-scale multi-objective optimization. In Proceedings of the 2016 Annual Genetic and Evolutionary Computation Conference Companion. ACM, New York, NY, 83–84. 10.1145/2908961.2908979 Bin Wang, Yanan Sun, Bing Xue, and Mengjie Zhang. Evolving deep neural networks by multi-objective particle swarm optimization for image classification. In Proceedings of the 2019 Annual Genetic and Evolutionary Computation Conference. ACM, New York, NY. 10.1145/3321707.3321735 Yaochu Jin and J. Branke. 2005. Evolutionary optimization in uncertain environments—A survey. IEEE Transactions on Evolutionary Computation 9, 3 (2005), 303–317. 10.1109/TEVC.2005.846356 Luis Miguel Antonio and Carlos A. Coello Coello. 2013. Use of cooperative coevolution for solving large scale multiobjective optimization problems. In Proceedings of the 2013 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA, 2758–2765. Wenjing Hong, Ke Tang, Aimin Zhou, Hisao Ishibuchi, and Xin Yao. 2018. A scalable indicator-based evolutionary algorithm for large-scale multiobjective optimization. IEEE Transactions on Evolutionary Computation 23, 3 (2018), 525–537. Jia Liu, Maoguo Gong, Qiguang Miao, Xiaogang Wang, and Hao Li. 2017. Structure learning for deep neural networks based on multiobjective optimization. IEEE Transactions on Neural Networks and Learning Systems 29, 6 (2017), 2450–2463. Masanori Suganuma, Mete Ozay, and Takayuki Okatani. 2018. Exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search. Proceedings of Machine Learning Research 80 (2018), 4771–4780. K. C. Tan, Y. H. Chew, and L. H. Lee. 2006. A hybrid multi-objective evolutionary algorithm for solving truck and trailer vehicle routing problems. European Journal of Operational Research 172, 3 (2006), 855–885. 10.1007/s10589-005-3070-3 Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems. Curran Associates, 2672–2680. 10.5555/2969033.2969125 Sumanta Ray and Ujjwal Maulik. 2017. Identifying differentially coexpressed module during HIV disease progression: A multiobjective approach. Scientific Reports 7 (2017), 86. Nicola Beume, Boris Naujoks, and Michael Emmerich. 2007. SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research 181, 3 (2007), 1653–1669. Jiahai Wang, Wenbin Ren, Zizhen Zhang, Han Huang, and Yuren Zhou. 2020. A hybrid multiobjective memetic algorithm for multiobjective periodic vehicle routing problem with time windows. IEEE Transactions on Systems, Man, and Cybernetics: Systems 50, 11 (2020), 4732–4745. Ran Cheng, Yaochu Jin, Markus Olhofer, and Bernhard Sendhoff. 2017. Test problems for large-scale multiobjective and many-objective optimization. IEEE Transactions on Cybernetics 47, 12 (2017), 4108–4121. Tinkle Chugh, Karthik Sindhya, Jussi Hakanen, and Kaisa Miettinen. 2019. A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms. Soft Computing 23 (2019), 3137–3166. 10.1007/s00500-017-2965-0 Herbert Robbins and Sutton Monro. 1951. A stochastic approximation method. Annals of Mathematical Statistics 22, 3 (1951), 400–407. Deyvid Heric Moraes, Danilo Sipoli Sanches, Josimar da Silva Rocha, Jader Maikol, Caldonazzo Garbelini, and Marcelo Favoretto Castoldi. 2019. A novel multi-objective evolutionary algorithm based on subpopulations for the bi-objective traveling salesman problem. Soft Computing 23 (2019), 6157–6168. 10.1007/s00500-018-3269-8 Harry Markowitz. 1952. Portfolio selection. Journal of Finance 7, 1 (1952), 77–91. Hanxiao Liu, Karen Simonyan, and Yiming Yang. DARTS: Differentiable architecture search. In Proceedings e_1_2_1_199_1 Tian Ye (e_1_2_1_164_1) 2020 e_1_2_1_138_1 Jabir E. (e_1_2_1_72_1) 2015; 189 e_1_2_1_187_1 e_1_2_1_213_1 e_1_2_1_24_1 e_1_2_1_62_1 e_1_2_1_141_1 e_1_2_1_85_1 Streichert Felix (e_1_2_1_150_1) e_1_2_1_103_1 e_1_2_1_126_1 e_1_2_1_149_1 e_1_2_1_8_1 e_1_2_1_175_1 e_1_2_1_198_1 e_1_2_1_12_1 e_1_2_1_50_1 e_1_2_1_73_1 e_1_2_1_96_1 e_1_2_1_152_1 e_1_2_1_190_1 e_1_2_1_224_1 e_1_2_1_58_1 Sallam Karam M. (e_1_2_1_143_1) e_1_2_1_209_1 Miikkulainen Robert (e_1_2_1_115_1) e_1_2_1_114_1 e_1_2_1_137_1 e_1_2_1_163_1 e_1_2_1_40_1 e_1_2_1_186_1 e_1_2_1_212_1 e_1_2_1_63_1 e_1_2_1_86_1 e_1_2_1_140_1 e_1_2_1_25_1 Zhang Qingfu (e_1_2_1_128_1) 2008 e_1_2_1_125_1 e_1_2_1_148_1 e_1_2_1_7_1 e_1_2_1_174_1 e_1_2_1_197_1 e_1_2_1_51_1 e_1_2_1_97_1 e_1_2_1_200_1 e_1_2_1_151_1 e_1_2_1_223_1 e_1_2_1_36_1 e_1_2_1_59_1 e_1_2_1_208_1 Yue C. T. (e_1_2_1_201_1) e_1_2_1_155_1 e_1_2_1_178_1 e_1_2_1_117_1 e_1_2_1_87_1 Antonio Luis Miguel (e_1_2_1_4_1) e_1_2_1_215_1 Markowitz Harry (e_1_2_1_112_1) 1952; 7 e_1_2_1_120_1 e_1_2_1_181_1 e_1_2_1_64_1 e_1_2_1_49_1 e_1_2_1_26_1 Tian Ye (e_1_2_1_169_1) e_1_2_1_90_1 Yang Zhaohui (e_1_2_1_196_1) e_1_2_1_105_1 e_1_2_1_189_1 e_1_2_1_98_1 e_1_2_1_6_1 e_1_2_1_154_1 e_1_2_1_192_1 e_1_2_1_52_1 e_1_2_1_75_1 Tian Ye (e_1_2_1_166_1) 2020 e_1_2_1_14_1 e_1_2_1_226_1 e_1_2_1_80_1 e_1_2_1_116_1 e_1_2_1_139_1 e_1_2_1_177_1 Eberhart R. (e_1_2_1_41_1) e_1_2_1_42_1 e_1_2_1_88_1 e_1_2_1_165_1 e_1_2_1_214_1 e_1_2_1_180_1 e_1_2_1_142_1 e_1_2_1_27_1 Lin D. (e_1_2_1_100_1) e_1_2_1_91_1 e_1_2_1_104_1 e_1_2_1_188_1 He Shan (e_1_2_1_65_1) 2016; 20 e_1_2_1_127_1 e_1_2_1_30_1 e_1_2_1_76_1 e_1_2_1_5_1 Deb Kalyanmoy (e_1_2_1_32_1) 1996; 26 e_1_2_1_99_1 e_1_2_1_176_1 e_1_2_1_202_1 e_1_2_1_191_1 e_1_2_1_53_1 e_1_2_1_130_1 e_1_2_1_38_1 e_1_2_1_15_1 e_1_2_1_225_1 Ventresca Mario (e_1_2_1_173_1) e_1_2_1_119_1 Diederik (e_1_2_1_84_1) 2014 Grandinetti L. (e_1_2_1_56_1) 2014; 111 Zhang Lei (e_1_2_1_206_1) e_1_2_1_111_1 e_1_2_1_157_1 e_1_2_1_81_1 e_1_2_1_134_1 e_1_2_1_217_1 e_1_2_1_20_1 e_1_2_1_66_1 e_1_2_1_160_1 Zhang Chunkai (e_1_2_1_203_1) e_1_2_1_89_1 e_1_2_1_43_1 e_1_2_1_183_1 e_1_2_1_28_1 Liu Hanxiao (e_1_2_1_102_1) e_1_2_1_92_1 e_1_2_1_122_1 e_1_2_1_145_1 e_1_2_1_107_1 e_1_2_1_168_1 e_1_2_1_54_1 e_1_2_1_77_1 e_1_2_1_205_1 Antonio Luis Miguel (e_1_2_1_3_1) e_1_2_1_110_1 Suzuki Takahiro (e_1_2_1_156_1) e_1_2_1_171_1 e_1_2_1_194_1 e_1_2_1_16_1 e_1_2_1_39_1 e_1_2_1_82_1 e_1_2_1_133_1 e_1_2_1_118_1 e_1_2_1_179_1 Suganuma Masanori (e_1_2_1_153_1) 2018; 80 e_1_2_1_216_1 e_1_2_1_67_1 e_1_2_1_121_1 e_1_2_1_21_1 e_1_2_1_182_1 Pighetti Romaric (e_1_2_1_131_1) e_1_2_1_29_1 e_1_2_1_93_1 e_1_2_1_70_1 e_1_2_1_144_1 e_1_2_1_129_1 e_1_2_1_167_1 e_1_2_1_106_1 e_1_2_1_55_1 e_1_2_1_204_1 e_1_2_1_78_1 e_1_2_1_132_1 e_1_2_1_170_1 e_1_2_1_193_1 e_1_2_1_17_1 Fieldsend Jonathan E. (e_1_2_1_48_1) Shahsamandi Esfahani P. (e_1_2_1_44_1) 2017; 5 e_1_2_1_136_1 e_1_2_1_60_1 e_1_2_1_113_1 Bucur Doina (e_1_2_1_13_1) e_1_2_1_159_1 Tian Ye (e_1_2_1_162_1) 2019 e_1_2_1_185_1 e_1_2_1_68_1 e_1_2_1_45_1 e_1_2_1_83_1 e_1_2_1_211_1 e_1_2_1_22_1 e_1_2_1_219_1 Jayabarathi T. (e_1_2_1_74_1); 744 e_1_2_1_109_1 Deb Kalyanmoy (e_1_2_1_31_1) 1995; 9 e_1_2_1_124_1 e_1_2_1_147_1 e_1_2_1_71_1 Fan Zhun (e_1_2_1_47_1) 2019 e_1_2_1_79_1 e_1_2_1_10_1 e_1_2_1_33_1 e_1_2_1_94_1 e_1_2_1_2_1 e_1_2_1_222_1 e_1_2_1_18_1 e_1_2_1_207_1 Deng Yepeng (e_1_2_1_37_1) e_1_2_1_135_1 e_1_2_1_158_1 Deb Kalyanmoy (e_1_2_1_35_1) e_1_2_1_161_1 e_1_2_1_184_1 e_1_2_1_46_1 e_1_2_1_61_1 e_1_2_1_210_1 Shoaf J. (e_1_2_1_146_1) e_1_2_1_69_1 e_1_2_1_218_1 e_1_2_1_108_1 Li Xiaodong (e_1_2_1_95_1) 2013 e_1_2_1_123_1 Lin Qiuzhen (e_1_2_1_101_1) 2015; 247 e_1_2_1_195_1 e_1_2_1_57_1 e_1_2_1_34_1 e_1_2_1_1_1 e_1_2_1_11_1 e_1_2_1_172_1 e_1_2_1_221_1 e_1_2_1_9_1 e_1_2_1_19_1 |
| References_xml | – reference: Michelle Girvan and Mark E. J. Newman. 2002. Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America 99, 12 (2002), 7821–7826. – reference: Maoguo Gong, Jia Liu, Hao Li, Qing Cai, and Linzhi Su. 2015. A multiobjective sparse feature learning model for deep neural networks. IEEE Transactions on Neural Networks and Learning Systems 26, 12 (2015), 3263–3277. – reference: Wen Shi, Wei-Neng Chen, Ying Lin, Tianlong Gu, Sam Kwong, and Jun Zhang. 2019. An adaptive estimation of distribution algorithm for multipolicy insurance investment planning. IEEE Transactions on Evolutionary Computation 23, 1 (2019), 1–14. – reference: Seyedmohsen Hosseini and Abdullah Al Khaled. 2014. A survey on the imperialist competitive algorithm metaheuristic: Implementation in engineering domain and directions for future research. Applied Soft Computing 24 (2014), 1078–1094. 10.1016/j.asoc.2014.08.024 – reference: Ye Tian, Xiutao Zheng, Xingyi Zhang, and Yaochu Jin. 2020. Efficient large-scale multiobjective optimization based on a competitive swarm optimizer. IEEE Transactions on Cybernetics 50, 8 (2020), 3696–3708. – reference: Lei Zhang, Guanglong Fu, Fan Cheng, Jianfeng Qiu, and Yansen Su. 2018. A multi-objective evolutionary approach for mining frequent and high utility itemsets. Applied Soft Computing 62 (2018), 974–986. – reference: Yutao Qi, Zhanting Hou, He Li, Jianbin Huang, and Xiaodong Li. 2015. A decomposition based memetic algorithm for multi-objective vehicle routing problem with time windows. Computers & Operations Research 62 (2015), 61–77. 10.1016/j.cor.2015.04.009 – reference: Xiaoshu Xiang, Ye Tian, Jianhua Xiao, and Xingyi Zhang. 2020. A clustering-based surrogate-assisted multi-objective evolutionary algorithm for shelter location under uncertainty of road networks. IEEE Transactions on Industrial Informatics 16, 12 (2020), 7544–7555. – reference: Jian Xiong, Chao Zhang, Gang Kou, Rui Wang, Hisao Ishibuchi, and Fawaz E. Alsaadi. 2020. Optimizing long-term bank financial products portfolio problems with a multiobjective evolutionary approach. Complexity 2020 (2020), 3106097. – reference: D. Lin, S. Wang, and H. Yan. A multiobjective genetic algorithm for portfolio selection. In Proceedings of the 5th International Conference on Optimization: Techniques and Applications. – reference: Rainer Storn and Kenneth Price. 1997. Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11, 4 (1997), 341–359. 10.1023/A:1008202821328 – reference: Shreya Khare, Rahul Aralikatte, and Senthil Mani. 2018. Adversarial black-box attacks on automatic speech recognition systems using multi-objective evolutionary optimization. arXiv:1811.01312. – reference: Ye Tian, Cheng He, Ran Cheng, and Xingyi Zhang. 2019. A multistage evolutionary algorithm for better diversity preservation in multiobjective optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems (2019). – reference: Xingyi Zhang, Ye Tian, Ran Cheng, and Yaochu Jin. 2018. A decision variable clustering-based evolutionary algorithm for large-scale many-objective optimization. IEEE Transactions on Evolutionary Computation 22, 1 (2018), 97–112. – reference: Ye Tian, Tao Zhang, Jianhua Xiao, Xingyi Zhang, and Yaochu Jin. 2021. A coevolutionary framework for constrained multi-objective optimization problems. IEEE Transactions on Evolutionary Computation 25, 1 (2021), 102–116. – reference: Kalyanmoy Deb and Mayank Goyal. 1996. A combined genetic adaptive search (GeneAS) for engineering design. Computer Science and Informatics 26, 4 (1996), 30–45. – reference: Yong Wang, Kevin Assogba, Yong Liu, Xiaolei Ma, Maozeng Xu, and Yinhai Wang. 2018. Two-echelon location-routing optimization with time windows based on customer clustering. Expert Systems with Applications 104 (2018), 244–260. – reference: Aimin Zhou, Bo-Yang Qu, Hui Li, Shi-Zheng Zhao, Ponnuthurai Nagaratnam Suganthan, and Qingfu Zhang. 2011. Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation 1 (2011), 32–49. – reference: Hui Li, Qingfu Zhang, and Jingda Deng. 2016. Biased multiobjective optimization and decomposition algorithm. IEEE Transactions on Cybernetics 47, 1 (2016), 52–66. – reference: An Song, Qiang Yang, Wei-Neng Chen, and Jun Zhang. 2016. A random-based dynamic grouping strategy for large scale multi-objective optimization. In Proceedings of the 2016 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA, 468–475. – reference: E. Jabir, Vinay V. Panicker, and R. Sridharan. 2015. Multi-objective optimization model for a green vehicle routing problem. Procedia: Social and Behavioral Sciences 189, 15 (2015), 33–39. – reference: Nicolas Jozefowiez, Frédéric Semet, and El-Ghazali Talbi. 2002. Parallel and hybrid models for multi-objective optimization: Application to the vehicle routing problem. In Proceedings of the 2002 International Conference on Parallel Problem Solving from Nature. 10.5555/645826.669582 – reference: Shuai Wang, Jing Liu, and Yaochu Jin. 2020. Surrogate-assisted robust optimization of large-scale networks based on graph embedding. IEEE Transactions on Evolutionary Computation 24, 4 (2020), 735–749. – reference: Jiahai Wang, Taiyao Weng, and Qingfu Zhang. 2019. A two-stage multiobjective evolutionary algorithm for multiobjective multidepot vehicle routing problem with time windows. IEEE Transactions on Cybernetics 49, 7 (2019), 2467–2478. – reference: Jiahai Wang, Ying Zhou, Yong Wang, Jun Zhang, C. L. Philip Chen, and Zibin Zheng. 2016. Multiobjective vehicle routing problems with simultaneous delivery and pickup and time windows: Formulation, instances, and algorithms. IEEE Transactions on Cybernetics 46, 3 (2016), 582–594. – reference: Julian Blank, Kalyanmoy Deb, and Sanaz Mostaghim. Solving the bi-objective traveling thief problem with multi-objective evolutionary algorithms. In Proceedings of the 2017 International Conference on Evolutionary Multi-Criterion Optimization. 10.1007/978-3-319-54157-0_4 – reference: Indrajit Saha, Ujjwal Maulik, and Dariusz Plewczynski. 2011. A new multi-objective technique for differential fuzzy clustering. Applied Soft Computing 11, 2 (2011), 2765–2776. 10.1016/j.asoc.2010.11.007 – reference: Hongke Zhao, Yong Ge, Qi Liu, Guifeng Wang, Enhong Chen, and Hefu Zhang. 2017. P2P lending survey: Platforms, recent advances and prospects. ACM Transactions on Intelligent Systems and Technology 8, 6 (2017), 1–28. 10.1145/3078848 – reference: Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th International Conference on Machine Learning. ACM, New York, NY, 1096–1103. 10.1145/1390156.1390294 – reference: Cheng He, Ran Cheng, Chuanji Zhang, Ye Tian, Qin Chen, and Xin Yao. 2018. Evolutionary large-scale multiobjective optimization for ratio error estimation of voltage transformers. IEEE Transactions on Evolutionary Computation 24, 5 (2018), 868–881. – reference: Caitong Yue, Boyang Qu, and Jing Liang. 2018. A multiobjective particle swarm optimizer using ring topology for solving multimodal multiobjective problems. IEEE Transactions on Evolutionary Computation 22, 5 (2018), 805–817. – reference: Anil Kumar Jain, M. Narasimha Murty, and P. J. Flynn. 1999. Data clustering: A review. ACM Computing Surveys 31, 3 (1999), 264–323. 10.1145/331499.331504 – reference: Jing Liu, Yaxiong Chi, Chen Zhu, and Yaochu Jin. 2017. A time series driven decomposed evolutionary optimization approach for reconstructing large-scale gene regulatory networks based on fuzzy cognitive maps. BMC Bioinformatics 18 (2017), 241. – reference: Wei Du, Weimin Zhong, Yang Tang, Wenli Du, and Yaochu Jin. 2018. High-dimensional robust multi-objective optimization for order scheduling: A decision variable classification approach. IEEE Transactions on Industrial Informatics 15, 1 (2018), 293–304. – reference: Ye Tian, Ruchen Liu, Xingyi Zhang, Haiping Ma, Kay Chen Tan, and Yaochu Jin. 2021. A multi-population evolutionary algorithm for solving large-scale multi-modal multi-objective optimization problems. IEEE Transactions on Evolutionary Computation 25, 3 (2021), 405–418. – reference: Hui Li, Qingfu Zhang, Jingda Deng, and Zong-Ben Xu. 2018. A preference-based multiobjective evolutionary approach for sparse optimization. IEEE Transactions on Neural Networks and Learning Systems 29, 5 (2018), 1716–1731. – reference: T. Jayabarathi, T. Raghunathan, and A. H. Gandomi. 2018. The bat algorithm, variants and some practical engineering applications: A review. In Nature-Inspired Algorithms and Applied Optimization. Studies in Computational Intelligence, Vol. 744. Springer, 313–330. – reference: Jiao-Hong Yi, Li-Ning Xing, Gai-Ge Wang, Junyu Dong, Athanasios V. Vasilakos, Amir H. Alavi, and Ling Wang. 2020. Behavior of crossover operators in NSGA-III for large-scale optimization problems. Information Sciences 509 (2020), 470–487. – reference: Ran Cheng, Yaochu Jin, Markus Olhofer, and Bernhard Sendhoff. 2017. Test problems for large-scale multiobjective and many-objective optimization. IEEE Transactions on Cybernetics 47, 12 (2017), 4108–4121. – reference: Xingyi Zhang, Fuchen Duan, Lei Zhang, Fan Cheng, Yaochu Jin, and Ke Tang. 2017. Pattern recommendation in task-oriented applications: A multi-objective perspective [application notes]. IEEE Computational Intelligence Magazine 12, 3 (2017), 43–53. – reference: Carlos Alberto de Araújo Padilha, Dante Augusto Couto Barone, and Adriao Duarte Dória Neto. 2016. A multi-level approach using genetic algorithms in an ensemble of least squares support vector machines. Knowledge-Based Systems 106 (2016), 85–95. 10.1016/j.knosys.2016.05.033 – reference: Cheng He, Shihua Huang, Ran Cheng, Kay Chen Tan, and Yaochu Jin. 2021. Evolutionary multiobjective optimization driven by generative adversarial networks (GANs). IEEE Transactions on Cybernetics 51, 6 (2021), 3129–3142. – reference: Yannis Marinakis and Magdalene Marinaki. 2008. A particle swarm optimization algorithm with path relinking for the location routing problem. Journal of Mathematical Modelling and Algorithm 7 (2008), 59–78. – reference: Cheng He, Lianghao Li, Ye Tian, Xingyi Zhang, Ran Cheng, Yaochu Jin, and Xin Yao. 2019. Accelerating large-scale multiobjective optimization via problem reformulation. IEEE Transactions on Evolutionary Computation 23, 6 (2019), 949–961. – reference: Jundong Li, Kewei Cheng, Suhang Wang, Fred Morstatter, Robert P. Trevino, Jiliang Tang, and Huan Liu. 2018. Feature selection: A data perspective. ACM Computing Surveys 50, 6 (2018), 1–45. 10.1145/3136625 – reference: Matthias Ehrgott. 2005. Multicriteria Optimization. Springer Science & Business Media. 10.5555/1121732 – reference: Lei Zhang, Xinpeng Wu, Hongke Zhao, Fan Chen, and Qi Liu. 2020. Personalized recommendation in P2P lending based on risk-return management: A multi-objective perspective. IEEE Transactions on Big Data. Early access, May 8, 2020. – reference: Tinkle Chugh, Karthik Sindhya, Kaisa Miettinen, Yaochu Jin, Tomas Kratky, and Pekka Makkonen. 2017. Surrogate-assisted evolutionary multiobjective shape optimization of an air intake ventilation system. In Proceedings of the 2017 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA. – reference: Bach Hoai Nguyen, Bing Xue, Peter Andreae, Hisao Ishibuchi, and Mengjie Zhang. 2020. Multiple reference points-based decomposition for multiobjective feature selection in classification: Static and dynamic mechanisms. IEEE Transactions on Evolutionary Computation 24, 1 (2020), 170–184. – reference: Emrah Hancer, Bing Xue, and Mengjie Zhang. 2020. A survey on feature selection approaches for clustering. Artificial Intelligence Review 53 (2020), 4519–4545. – reference: Kalyanmoy Deb, Lothar Thiele, Marco Laumanns, and Eckart Zitzler. 2005. Scalable test problems for evolutionary multiobjective optimization. In Evolutionary Multiobjective Optimization. Springer, 105–145. – reference: Yepeng Deng, Chunkai Zhang, and Xuan Wang. A multi-objective examples generation approach to fool the deep neural networks in the black-box scenario. In Proceedings of the 2019 IEEE International Conference on Data Science in Cyberspace. IEEE, Los Alamitos, CA. – reference: Yangyang Li, Yang Wang, Jing Chen, Licheng Jiao, and Ronghua Shang. 2015. Overlapping community detection through an improved multi-objective quantum-behaved particle swarm optimization. Journal of Heuristics 21, 4 (2015), 549–575. 10.1007/s10732-015-9289-y – reference: Peiqiu Huang and Yong Wang. 2020. A framework for scalable bilevel optimization: Identifying and utilizing the interactions between upper-level and lower-level variables. IEEE Transactions on Evolutionary Computation 24, 6 (2020), 1150–1163. – reference: Mohammed Lalou, Mohammed Amin Tahraoui, and Hamamache Kheddouci. 2018. The critical node detection problem in networks: A survey. Computer Science Review 28 (2018), 92–117. – reference: Simon Huband, Philip Hingston, Luigi Barone, and Lyndon While. 2006. A review of multiobjective test problems and a scalable test problem toolkit. IEEE Transactions on Evolutionary Computation 10, 5 (2006), 477–506. 10.1109/TEVC.2005.861417 – reference: Wenxiang Chen, Thomas Weise, Zhenyu Yang, and Ke Tang. 2010. Large-scale global optimization using cooperative coevolution with variable interaction learning. In Proceedings of the 2010 International Conference on Parallel Problem Solving from Nature. 300–309. 10.5555/1887255.1887289 – reference: Mario Ventresca, Kyle Robert Harrison, and Beatrice M. Ombuki-Berman. An experimental evaluation of multi-objective evolutionary algorithms for detecting critical nodes in complex networks. In Proceedings of the 2015 European Conference on the Applications of Evolutionary Computation. – reference: Mario Garza-Fabre, Julia Handl, and Joshua Damian Knowles. 2018. An improved and more scalable evolutionary approach to multiobjective clustering. IEEE Transactions on Evolutionary Computation 22, 4 (2018), 515–535. – reference: Heiner Zille and Sanaz Mostaghim. Comparison study of large-scale optimisation techniques on the LSMOP benchmark functions. In Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence. – reference: Iris Abril Martínez-Salazar, Julian Molina, Francisco Ángel Bello, Trinidad Gómez, and Rafael Caballero. 2014. Solving a bi-objective transportation location routing problem by metaheuristic algorithms. European Journal of Operational Research 234 (2014), 25–36. – reference: Jun-Rong Jian, Zhi-Hui Zhan, and Jun Zhang. 2020. Large-scale evolutionary optimization: A survey and experimental comparative study. International Journal of Machine Learning and Cybernetics 11 (2020), 729–745. – reference: Ye Tian, Shangshang Yang, Xingyi Zhang, and Yaochu Jin. Using PlatEMO to solve multi-objective optimization problems. In Proceedings of the 2019 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA. – reference: Bin Wang, Yanan Sun, Bing Xue, and Mengjie Zhang. Evolving deep neural networks by multi-objective particle swarm optimization for image classification. In Proceedings of the 2019 Annual Genetic and Evolutionary Computation Conference. ACM, New York, NY. 10.1145/3321707.3321735 – reference: Hongke Zhao, Qi Liu, Guifeng Wang, Yong Ge, and Enhong Chen. Portfolio selections in P2P lending: A multi-objective perspective. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY. 10.1145/2939672.2939861 – reference: Jia Liu, Maoguo Gong, Qiguang Miao, Xiaogang Wang, and Hao Li. 2017. Structure learning for deep neural networks based on multiobjective optimization. IEEE Transactions on Neural Networks and Learning Systems 29, 6 (2017), 2450–2463. – reference: Zhun Fan, Wenji Li, Xinye Cai, Li Hui, and Erik D. Goodman. 2019. Push and pull search for solving constrained multi-objective optimization problems. Swarm and Evolutionary Computation44 (2019), 665–679. – reference: Ye Tian, Shangshang Yang, Lei Zhang, Fuchen Duan, and Xingyi Zhang. 2019. A surrogate-assisted multiobjective evolutionary algorithm for large-scale task-oriented pattern mining. IEEE Transactions on Emerging Topics in Computational Intelligence 3, 2 (2019), 106–116. – reference: Zhangtao Li, Jing Liu, and Kai Wu. 2017. A multiobjective evolutionary algorithm based on structural and attribute similarities for community detection in attributed networks. IEEE Transactions on Cybernetics 48, 7 (2017), 1963–1976. – reference: Shufen Qin, Chaoli Sun, Yaochu Jin, Ying Tan, and Jonathan Fieldsend. 2021. Large-scale evolutionary multi-objective optimization assisted by directed sampling. IEEE Transactions on Evolutionary Computation. Early access, March 3, 2021. – reference: Nikolaus Hansen and Andreas Ostermeier. 2001. Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation 9, 2 (2001), 159–195. 10.1162/106365601750190398 – reference: Seyedali Mirjalili and Andrew Lewis. 2016. The whale optimization algorithm. Advances in Engineering Software 95 (2016), 51–67. 10.1016/j.advengsoft.2016.01.008 – reference: Thibaut Lust and Jacques Teghem. 2009. Two-phase Pareto local search for the biobjective traveling salesman problem. Journal of Heuristics 16 (2009), 475–510. 10.1007/s10732-009-9103-9 – reference: Robert Miikkulainen, Cesare Alippi, Yoonsuck Choe, Francesco, and Carlo Morabito. 2019. Evolving deep neural networks. In Artificial Intelligence in the Age of Neural Networks and Brain Computing. Academic Press, 293–312. – reference: Kalyanmoy Deb and Himanshu Jain. 2013. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation 18, 4 (2013), 577–601. – reference: N. Nekooghadirli, R. Tavakkoli-Moghaddam, V. R. Ghezavati and S. Javanmard. 2014. Solving a new bi-objective location-routing-inventory problem in a distribution network by meta-heuristics. Computers & Industrial Engineering 76 (2014), 204–221. 10.1016/j.cie.2014.08.004 – reference: Clara Pizzuti. 2012. A multiobjective genetic algorithm to find communities in complex networks. IEEE Transactions on Evolutionary Computation 16, 3 (2012), 418–430. 10.1109/TEVC.2011.2161090 – reference: Nele Verbiest, Joaquín Derrac, Chris Cornelis, Salvador García, and Francisco Herrera. 2016. Evolutionary wrapper approaches for training set selection as preprocessing mechanism for support vector machines: Experimental evaluation and support vector analysis. Applied Soft Computing 38 (2016), 10–22. 10.5555/2873822.2873894 – reference: Alejandro Rosales-Pérez, Salvador García, Jesus A. Gonzalez, Carlos A. Coello Coello, and Francisco Herrera. 2017. An evolutionary multiobjective model and instance selection for support vector machines with Pareto-based ensembles. IEEE Transactions on Evolutionary Computation 21, 6 (2017), 863–877. – reference: Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele. 2000. Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8, 2 (2000), 173–195. 10.1162/106365600568202 – reference: J. Handl and Joshua Damian Knowles. 2007. An evolutionary approach to multiobjective clustering. IEEE Transactions on Evolutionary Computation 11, 1 (2007), 56–76. 10.1109/TEVC.2006.877146 – reference: Bin Cao, Jianwei Zhao, Zhihan Lv, and Xin Liu. 2017. A distributed parallel cooperative coevolutionary multiobjective evolutionary algorithm for large-scale optimization. IEEE Transactions on Industrial Informatics 13, 4 (2017), 2030–2038. – reference: Vivek K. Patel and Vimal J. Savsani. 2015. Heat transfer search (HTS): A novel optimization algorithm. Information Sciences 324 (2015), 217–246. 10.1016/j.ins.2015.06.044 – reference: Antonio J. Nebro, Juan José Durillo, Jose Garcia-Nieto, C. A. Coello Coello, Francisco Luna, and Enrique Alba. 2009. SMPSO: A new PSO-based metaheuristic for multi-objective optimization. In Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making. IEEE, Los Alamitos, CA, 66–73. – reference: Huangke Chen, Ran Cheng, Jinming Wen, Haifeng Li, and Jian Weng. 2020. Solving large-scale many-objective optimization problems by covariance matrix adaptation evolution strategy with scalable small subpopulations. Information Sciences 509 (2020), 457–469. – reference: M. T. M. Emmerich, K. C. Giannakoglou, and B. Naujoks. 2006. Single- and multi-objective evolutionary optimization assisted by Gaussian random field metamodels. IEEE Transactions on Evolutionary Computation 10, 4 (2006), 421–439. 10.1109/TEVC.2005.859463 – reference: Andrea Paoli, Farid Melgani, and Edoardo Pasolli. 2009. Clustering of hyperspectral images based on multiobjective particle swarm optimization. IEEE Transactions on Geoscience and Remote Sensing 47, 12 (2009), 4175–4188. – reference: Ye Tian, Shangshang Yang, and Xingyi Zhang. 2020. An evolutionary multiobjective optimization based fuzzy method for overlapping community detection. IEEE Transactions on Fuzzy Systems 28, 11 (2020), 2841–2855. – reference: Luis Miguel Antonio and Carlos A. Coello Coello. 2016. Decomposition-based approach for solving large scale multi-objective problems. In Proceedings of the International Conference on Parallel Problem Solving from Nature. 525–534. – reference: Harry Markowitz. 1952. Portfolio selection. Journal of Finance 7, 1 (1952), 77–91. – reference: Jose Caceres-Cruz, Pol Arias, Daniel Guimarans, Daniel Riera, and Angel A. Juan. 2015. Rich vehicle routing problem: Survey. ACM Computing Surveys 47, 2 (2015), 1–28. 10.1145/2666003 – reference: Ran Cheng and Yaochu Jin. 2015. A competitive swarm optimizer for large scale optimization. IEEE Transactions on Cybernetics 45, 2 (2015), 191–204. – reference: Huangke Chen, Xiaomin Zhu, Witold Pedrycz, Shu Yin, Guohua Wu, and Hui Yan. 2018. PEA: Parallel evolutionary algorithm by separating convergence and diversity for large-scale multi-objective optimization. In Proceedings of the 2018 IEEE International Conference on Distributed Computing Systems. IEEE, Los Alamitos, CA, 223–232. – reference: Heiner Zille, Hisao Ishibuchi, Sanaz Mostaghim, and Yusuke Nojima. 2016. Mutation operators based on variable grouping for multi-objective large-scale optimization. In Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence. IEEE, Los Alamitos, CA. – reference: Lei Zhang, Jiajun Xia, Fan Cheng, Jianfeng Qiu, and Xingyi Zhang. 2020. Multi-objective optimization of critical node detection based on cascade model in complex networks. IEEE Transactions on Network Science and Engineering 7, 3 (2020), 2052–2066. – reference: Frederick Sander, Heiner Zille, and Sanaz Mostaghim. 2018. Transfer strategies from single- to multi-objective grouping mechanisms. In Proceedings of the 2018 Annual Genetic and Evolutionary Computation Conference. ACM, New York, NY, 729–736. 10.1145/3205455.3205491 – reference: Herbert Robbins and Sutton Monro. 1951. A stochastic approximation method. Annals of Mathematical Statistics 22, 3 (1951), 400–407. – reference: Luigi Amoroso. 1938. Vilfredo pareto. Econometrica 6, 1 (1938), 1–21. – reference: Felix Streichert, Holger Ulmer, and Andreas Zell. Comparing discrete and continuous genotypes on the constrained portfolio selection problem. In Proceedings of the 2004 Genetic and Evolutionary Computation Conference. – reference: Siripen Wikaisuksakul. 2014. A multi-objective genetic algorithm with fuzzy c-means for automatic data clustering. Applied Soft Computing 24 (2014), 679–691. 10.1016/j.asoc.2014.08.036 – reference: Heiner Zille and Sanaz Mostaghim. 2019. Linear search mechanism for multi- and many-objective optimisation. In Proceedings of the 2019 International Conference on Evolutionary Multi-Criterion Optimization. 399–410. – reference: Tsung-Che Chiang and Wei-Huai Hsu. 2014. A knowledge-based evolutionary algorithm for the multiobjective vehicle routing problem with time windows. Computers & Operations Research 45 (2014), 25–37. 10.1016/j.cor.2013.11.014 – reference: David Kempe, Jon Kleinberg, and Éva Tardos. Maximizing the spread of influence through a social network. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY. 10.1145/956750.956769 – reference: Ye Tian, Chang Lu, Xingyi Zhang, Fan Cheng, and Yaochu Jin. 2020. A pattern mining based evolutionary algorithm for large-scale sparse multi-objective optimization problems. IEEE Transactions on Cybernetics. Early access, December 30, 2020. – reference: Luis Miguel Antonio and Carlos A. Coello Coello. 2013. Use of cooperative coevolution for solving large scale multiobjective optimization problems. In Proceedings of the 2013 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA, 2758–2765. – reference: Bing Xue, Mengjie Zhang, and Will N. Browne. 2013. Particle swarm optimization for feature selection in classification: A multi-objective approach. IEEE Transactions on Cybernetics 43, 6 (2013), 1656–1671. – reference: Xin Yao. 1993. A review of evolutionary artificial neural networks. International Journal of Intelligent Systems 8, 4 (1993), 539–567. – reference: Mardé Helbig and Andries P. Engelbrecht. 2014. Benchmarks for dynamic multi-objective optimisation algorithms. ACM Computing Surveys 46, 3 (2014), 37. 10.1145/2517649 – reference: Sameer Singh, Jeremy Kubica, Scott Larsen, and Daria Sorokina. 2009. Parallel large scale feature selection for logistic regression. In Proceedings of the 2009 SIAM International Conference on Data Mining. – reference: Chengtao Li, David Alvarez-Melis, Keyulu Xu, Stefanie Jegelka, and Suvrit Sra. 2017. Distributional adversarial networks. arXiv:1706.09549. – reference: Kyong Joo Oh, Tae Yoon Kim, and Sungky Min. 2005. Using genetic algorithm to support portfolio optimization for index fund management. Expert Systems with Applications 28 (2005), 371–379. 10.1016/j.eswa.2004.10.014 – reference: Zhenyu Yang, Ke Tang, and Xin Yao. 2008. Large scale evolutionary optimization using cooperative coevolution. Information Sciences 178, 15 (2008), 2985–2999. 10.1016/j.ins.2008.02.017 – reference: Deyvid Heric Moraes, Danilo Sipoli Sanches, Josimar da Silva Rocha, Jader Maikol, Caldonazzo Garbelini, and Marcelo Favoretto Castoldi. 2019. A novel multi-objective evolutionary algorithm based on subpopulations for the bi-objective traveling salesman problem. Soft Computing 23 (2019), 6157–6168. 10.1007/s00500-018-3269-8 – reference: Mengjie Zhao, Kai Zhang, Guodong Chen, Xinggang Zhao, Chuanjin Yao, Hai Sun, Zhaoqin Huang, and Jun Yao. 2020. A surrogate-assisted multi-objective evolutionary algorithm with dimension-reduction for production optimization. Journal of Petroleum Science and Engineering 192 (2020), 107192. – reference: Jiahai Wang, Wenbin Ren, Zizhen Zhang, Han Huang, and Yuren Zhou. 2020. A hybrid multiobjective memetic algorithm for multiobjective periodic vehicle routing problem with time windows. IEEE Transactions on Systems, Man, and Cybernetics: Systems 50, 11 (2020), 4732–4745. – reference: Hong Qian and Yang Yu. 2017. Solving high-dimensional multi-objective optimization problems with low effective dimensions. In Proceedings of the 31st AAAI Conference on Artificial Intelligence. 875–881. 10.5555/3298239.3298367 – reference: Shengxiang Yang, Miqing Li, Xiaohui Liu, and Jinhua Zheng. 2013. A grid-based evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation 17, 5 (2013), 721–736. 10.1109/TEVC.2012.2227145 – reference: Kalyanmoy Deb and Santosh Tiwari. 2008. Omni-optimizer: A generic evolutionary algorithm for single and multi-objective optimization. European Journal of Operational Research 185 (2008), 1062–1087. – reference: Imen Harbaoui Dridi, Ryan Kammarti, Mekki Ksouri, and Pierre Borne. 2011. Multi-objective optimization for the m-PDPTW: Aggregation method with use of genetic algorithm and lower bounds. International Journal of Computers, Communications & Control 6, 2 (2011), 246–257. – reference: Zhenyu Liang, Yunfan Li, and Zhongwei Wan. 2020. Large scale many-objective optimization driven by distributional adversarial networks. arXiv:2003.07013. – reference: Yanan Sun, Bing Xue, Mengjie Zhang, and Gary G. Yen. 2018. A new two-stage evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation 23, 5 (2018), 748–761. – reference: Jia Wang, Yuchao Su, Qiuzhen Lin, Lijia Ma, Dunwei Gong, Jianqiang Li, and Zhong Ming. 2020. A survey of decomposition approaches in multiobjective evolutionary algorithms. Neurocomputing 408 (2020), 308–330. – reference: Shangshang Yang, Ye Tian, Cheng He, Xingyi Zhang, Kay Chen Tan, and Yaochu Jin. 2021. Gradient guided evolutionary approach to training deep neural networks. IEEE Transactions on Neural Networks and Learning Systems. Early access, March 4, 2021. – reference: Kalyanmoy Deb and Ram Bhusan Agrawal. 1995. Simulated binary crossover for continuous search space. Complex Systems 9, 4 (1995), 115–148. – reference: Bin Cao, Jianwei Zhao, Yu Gu, Yingbiao Ling, and Xiaoliang Ma. 2020. Applying graph-based differential grouping for multiobjective large-scale optimization. Swarm and Evolutionary Computation 53 (2020), 100626. – reference: Qing Cai, Lijia Ma, and Maoguo Gong. 2014. A survey on network community detection based on evolutionary computation. International Journal of Bio-Inspired Computation 8, 2 (2014), 84–98. 10.1504/IJBIC.2016.076329 – reference: Yosef Masoudi-Sobhanzadeh, Yadollah Omidi, Massoud Amanlou, and Ali Masoudi-Nejad. 2019. Trader as a new optimization algorithm predicts drug-target interactions efficiently. Scientific Reports 9 (2019), 9348. – reference: Jonathan E. Fieldsend, John Matatko, and Ming Peng. Cardinality constrained portfolio optimisation. In Proceedings of the 5th International Conference on Intelligent Data Engineering and Automated Learning. – reference: Maoguo Gong, Qing Cai, Xiaowei Chen, and Lijia Ma. 2014. Complex network clustering by multiobjective discrete particle swarm optimization based on decomposition. IEEE Transactions on Evolutionary Computation 18, 1 (2014), 82–97. – reference: Ryoji Tanabe and Alex Fukunaga. 2013. Success-history based parameter adaptation for differential evolution. In Proceedings of the 2013 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA. – reference: Zhun Fan, Yi Fang, Wenji Li, Jiewei Lu, and Xinye Cai. 2017. A comparative study of constrained multi-objective evolutionary algorithms on constrained multi-objective optimization problems. In Proceedings of the 2017 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA. – reference: Jesús Guillermo Falcón-Cardona and Carlos A. Coello Coello. 2020. Indicator-based multi-objective evolutionary algorithms: A comprehensive survey. ACM Computing Surveys 53, 2 (2020), 1–35. 10.1145/3376916 – reference: Hanxiao Liu, Karen Simonyan, and Yiming Yang. DARTS: Differentiable architecture search. In Proceedings of the 7th International Conference on Learning Representations. – reference: Mohammad Nabi Omidvar, Xiaodong Li, Yi Mei, and Xin Yao. 2014. Cooperative co-evolution with differential grouping for large scale optimization. IEEE Transactions on Evolutionary Computation 18, 3 (2014), 378–393. – reference: Seyedali Mirjalili. 2016. SCA: A sine cosine algorithm for solving optimization problems. Knowledge-Based Systems 96 (2016), 120–133. 10.1016/j.knosys.2015.12.022 – reference: J. Shoaf and J. A. Foster. The efficient set GA for stock portfolios. In Proceedings of the 1998 IEEE International Conference on Evolutionary Computation. IEEE, Los Alamitos, CA. – reference: Ryoji Tanabe and Hisao Ishibuchi. 2020. A review of evolutionary multi-modal multi-objective optimization. IEEE Transactions on Evolutionary Computation 24, 1 (2020), 193–200. – reference: Ye Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin. 2017. PlatEMO: A MATLAB platform for evolutionary multi-objective optimization [educational forum]. IEEE Computational Intelligence Magazine 12, 4 (2017), 73–87. – reference: Yiping Liu, Gary G. Yen, and Dunwei Gong. 2019. A multimodal multiobjective evolutionary algorithm using two-archive and recombination strategies. IEEE Transactions on Evolutionary Computation 23, 4 (2019), 660–674. – reference: Zhihong Li, Lanteng Wu, and Hongting Tang. 2018. Optimizing the borrowing limit and interest rate in P2P system: From borrowers’ perspective. Scientific Programming 2018 (2018), 2613739. – reference: P. Shahsamandi Esfahani and A. Saghaei. 2017. A multi-objective approach to fuzzy clustering using ITLBO algorithm. Journal of AI and Data Mining 5, 2 (2017), 307–317. – reference: Cheng He, Ran Cheng, Ye Tian, and Xingyi Zhang. 2020. Iterated problem reformulation for evolutionary large-scale multiobjective optimization. In Proceedings of the 2020 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA. – reference: Sumanta Ray and Ujjwal Maulik. 2017. Identifying differentially coexpressed module during HIV disease progression: A multiobjective approach. Scientific Reports 7 (2017), 86. – reference: Ye Tian, Xiaochun Su, Yansen Su, and Xingyi Zhang. 2020. EMODMI: A multi-objective optimization based method to identify disease modules. IEEE Transactions on Emerging Topics in Computational Intelligence. Early access, August 21, 2020. – reference: Saúl Zapotecas-Martínez, Carlos A. Coello Coello, Hernán E. Aguirre, and Kiyoshi Tanaka. 2019. A review of features and limitations of existing scalable multiobjective test suites. IEEE Transactions on Evolutionary Computation 23, 1 (2019), 130–142. – reference: Wei Du, Le Tong, and Yang Tang. 2018. A framework for high-dimensional robust evolutionary multi-objective optimization. In Proceedings of the 2018 Annual Genetic and Evolutionary Computation Conference. ACM, New York, NY, 1791–1796. 10.1145/3205651.3208243 – reference: Romaric Pighetti, Denis Pallez, and Frédéric Precioso. Improving SVM training sample selection using multi-objective evolutionary algorithm and LSH. In Proceedings of the 2015 IEEE Symposium on Computational Intelligence and Data Mining. IEEE, Los Alamitos, CA. – reference: Karam M. Sallam, Saber M. Elsayed, Ripon K. Chakrabortty, and Michael J. Ryan. 2020. Improved multi-operator differential evolution algorithm for solving unconstrained problems. In Proceedings of the IEEE Congress on Evolutionary Computation. – reference: Yanan Sun, Gary G. Yen, and Zhang Yi. 2019. Evolving unsupervised deep neural networks for learning meaningful representations. IEEE Transactions on Evolutionary Computation 23, 1 (2019), 89–103. – reference: Lei Zhang, Hebin Pan, Yansen Su, and Xingyi Zhang. 2014. A mixed representation based multi-objective evolutionary algorithm for overlapping community detection. IEEE Transactions on Cybernetics 47, 9 (2014), 2703–2716. – reference: Heiner Zille. 2019. Large-Scale Multi-Objective Optimisation: New Approaches and a Classification of the State-of-the-Art. Ph.D. Dissertation. Otto-von-Guericke-Universität Magdeburg, Fakultät für Informatik. – reference: Yaochu Jin and Bernhard Sendhoff. 2008. Pareto-based multiobjective machine learning: An overview and case studies. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 38, 3 (2008), 397–415. 10.1109/TSMCC.2008.919172 – reference: Ruochen Liu, Jin Liu, Yifan Li, and Jing Liu. 2020. A random dynamic grouping based weight optimization framework for large-scale multi-objective optimization problems. Swarm and Evolutionary Computation 55 (2020), 100684. – reference: Xingyi Zhang, Kefei Zhou, Hebin Pan, Lei Zhang, Xiangxiang Zeng, and Yaochu Jin. 2020. A network reduction-based multiobjective evolutionary algorithm for community detection in large-scale complex networks. IEEE Transactions on Cybernetics 50, 2 (2020), 703–716. – reference: Yousef Abdi and Mohammad-Reza Feizi-Derakhshi. 2020. Hybrid multi-objective evolutionary algorithm based on search manager framework for big data optimization problems. Applied Soft Computing 87 (2020), 105991. – reference: Saku Kukkonen and Jouni Lampinen. 2005. GDE3: The third evolution step of generalized differential evolution. In Proceedings of the 2005 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA, 443–450. – reference: Minghan Li and Jingxuan Wei. 2018. A cooperative co-evolutionary algorithm for large-scale multi-objective optimization problems. In Proceedings of the 2018 Annual Genetic and Evolutionary Computation Conference. ACM, New York, NY, 1716–1721. 10.1145/3205651.3208250 – reference: Kaiwen Li, Tao Zhang, and Rui Wang. 2021. Deep reinforcement learning for multiobjective optimization. IEEE Transactions on Cybernetics 51, 6 (2021), 3103–3114. – reference: Masanori Suganuma, Mete Ozay, and Takayuki Okatani. 2018. Exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search. Proceedings of Machine Learning Research 80 (2018), 4771–4780. – reference: K. C. Tan, Y. H. Chew, and L. H. Lee. 2006. A hybrid multi-objective evolutionary algorithm for solving truck and trailer vehicle routing problems. European Journal of Operational Research 172, 3 (2006), 855–885. 10.1007/s10589-005-3070-3 – reference: Yi Xiang, Yuren Zhou, Zibin Zheng, and Miqing Li. 2018. Configuring software product lines by combining many-objective optimization and SAT solvers. ACM Transactions on Software Engineering and Methodology 26, 4 (2018), 14. 10.1145/3176644 – reference: C. T. Yue, J. J. Liang, B. Y. Qu, K. J. Yu, and H. Song. 2019. Multimodal multiobjective optimization in feature selection. In Proceedings of the 2019 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA. – reference: Lei Zhang, Shangshang Yang, Xinpeng Wu, Fan Cheng, Ying Xie, and Zhiting Lin. 2019. An indexed set representation based multi-objective evolutionary approach for mining diversified top-k high utility patterns. Engineering Applications of Artificial Intelligence 77 (2019), 9–20. – reference: Cheng-Hong Yang, Li-Yeh Chuang, and Yu-Da Lin. 2017. Multiobjective differential evolution-based multifactor dimensionality reduction for detecting genegene interactions. Scientific Reports 7 (2017), 12869. – reference: Ran Cheng, Yaochu Jin, Kaname Narukawa, and Bernhard Sendhoff. 2015. A multiobjective evolutionary algorithm using Gaussian process-based inverse modeling. IEEE Transactions on Evolutionary Computation 19, 6 (2015), 838–856. – reference: Ran Cheng. 2016. Nature Inspired Optimization of Large Problems. Ph.D. Dissertation. University of Surrey. – reference: Ye Tian, Xingyi Zhang, Chao Wang, and Yaochu Jin. 2020. An evolutionary algorithm for large-scale sparse multi-objective optimization problems. IEEE Transactions on Evolutionary Computation 24, 2 (2020), 380–393. – reference: Xiaoliang Ma, Fang Liu, Yutao Qi, Xiaodong Wang, Lingling Li, Licheng Jiao, Minglei Yin, and Maoguo Gong. 2016. A multiobjective evolutionary algorithm based on decision variable analyses for multiobjective optimization problems with large-scale variables. IEEE Transactions on Evolutionary Computation 20, 2 (2016), 275–298. – reference: Irwan Bello, Barret Zoph, Vijay Vasudevan, and Quoc V. Le. Neural architecture search with reinforcement learning. In Proceedings of the 5th International Conference on Learning Representations. 10.5555/3305381.3305429 – reference: Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems. Curran Associates, 2672–2680. 10.5555/2969033.2969125 – reference: Xin Yao. 1999. Evolving artificial neural networks. Proceedings of the IEEE 87, 9 (1999), 1423–1447. – reference: Fan Cheng, Jiabin Chen, Jianfeng Qiu, and Lei Zhang. 2020. A subregion division based multi-objective evolutionary algorithm for SVM training set selection. Neurocomputing 394 (2020), 70–83. – reference: Doina Bucur, Giovanni Iacca, Andrea Marcelli, Giovanni Squillero, and Alberto Tonda. Multi-objective evolutionary algorithms for influence maximization in social networks. In Proceedings of the 2017 European Conference on the Applications of Evolutionary Computation. – reference: Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv:1412.6980. – reference: Nicolas Jozefowiez, Frédéric Semet, and El-Ghazali Talbi. 2008. Multi-objective vehicle routing problems. European Journal of Operational Research 189, 2 (2008), 293–309. – reference: L. Grandinetti, F. Guerriero, F. Pezzella, and O. Pisacane. 2014. The multi-objective multi-vehicle pickup and delivery problem with time windows. Procedia: Social and Behavioral Sciences 111, 5 (2014), 203–212. – reference: P. Larra Naga, C. M. H. Kuijpers, R. H. Murga, I. Inza, and S. Dizdarevic. 1999. Genetic algorithms for the travelling salesman problem: A review of representations and operators. Artificial Intelligence Review 13, 2 (1999), 129–170. 10.1023/A:1006529012972 – reference: Bingdong Li, Jinlong Li, Ke Tang, and Xin Yao. 2015. Many-objective evolutionary algorithms: A survey. ACM Computing Surveys 48, 1 (2015), 13. 10.1145/2792984 – reference: Wenjing Hong, Ke Tang, Aimin Zhou, Hisao Ishibuchi, and Xin Yao. 2018. A scalable indicator-based evolutionary algorithm for large-scale multiobjective optimization. IEEE Transactions on Evolutionary Computation 23, 3 (2018), 525–537. – reference: Ruochen Liu, Rui Ren, Jin Liu, and Jing Liu. 2020. A clustering and dimensionality reduction based evolutionary algorithm for large-scale multi-objective problems. Applied Soft Computing 89 (2020), 106120. – reference: Heiner Zille, Hisao Ishibuchi, Sanaz Mostaghim, and Yusuke Nojima. 2016. Weighted optimization framework for large-scale multi-objective optimization. In Proceedings of the 2016 Annual Genetic and Evolutionary Computation Conference Companion. ACM, New York, NY, 83–84. 10.1145/2908961.2908979 – reference: G. Thippa Reddy, M. Praveen Kumar Reddy, Kuruva Lakshmanna, Rajesh Kaluri, Dharmendra Singh Rajput, Gautam Srivastava, and Thar Baker. 2020. Analysis of dimensionality reduction techniques on big data. IEEE Access 8 (2020), 54776–54788. – reference: Xinye Cai, Yexing Li, Zhun Fan, and Qingfu Zhang. 2015. An external archive guided multiobjective evolutionary algorithm based on decomposition for combinatorial optimization. IEEE Transactions on Evolutionary Computation 19, 4 (2015), 508–523. – reference: Takahiro Suzuki, Shingo Takeshita, and Satoshi Ono. Adversarial example generation using evolutionary multi-objective optimization. In Proceedings of the 2019 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA. – reference: Marcin Suchorzewski and Jeff Clune. 2011. A novel generative encoding for evolving modular, regular and scalable networks. In Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation. ACM, New York, NY. 10.1145/2001576.2001781 – reference: Linpeng Tang, Lei Zhang, Ping Luo, and Min Wang. Incorporating occupancy into frequent pattern mining for high quality pattern recommendation. In Proceedings of the 21th ACM International Conference on Information and Knowledge Management. ACM, New York, NY. 10.1145/2396761.2396775 – reference: Urvesh Bhowan, Mark Johnston, and Mengjie Zhang. Ensemble learning and pruning in multi-objective genetic programming for classification with unbalanced data. In Proceedings of the 24th Australasian Joint Conference on Artificial Intelligence. 10.1007/978-3-642-25832-9_20 – reference: Chao Qian, Yang Yu, and Zhihua Zhou. Pareto ensemble pruning. In Proceedings of the 29th AAAI Conference on Artificial Intelligence. 10.5555/2888116.2888125 – reference: Hiba Bederina and Mhand Hifi. 2018. A hybrid multi-objective evolutionary optimization approach for the robust vehicle routing problem. Applied Soft Computing 71 (2018), 980–993. – reference: Tinkle Chugh, Karthik Sindhya, Jussi Hakanen, and Kaisa Miettinen. 2019. A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms. Soft Computing 23 (2019), 3137–3166. 10.1007/s00500-017-2965-0 – reference: Hang Xu, Bin Xue, and Mengjie Zhang. 2021. A duplication analysis based evolutionary algorithm for bi-objective feature selection. IEEE Transactions on Evolutionary Computation 25, 2 (2021), 205–218. – reference: Ke Li, Renzhi Chen, Guangtao Fu, and Xin Yao. 2018. Two-archive evolutionary algorithm for constrained multi-objective optimization. IEEE Transactions on Evolutionary Computation 23, 2 (2018), 303–315. – reference: Luis Miguel Antonio, Carlos A. Coello Coello, Silvia González Brambila, Josué Figueroa González, and Guadalupe Castillo Tapia. 2019. Operational decomposition for large scale multi-objective optimization problems. In Proceedings of the 2019 Annual Genetic and Evolutionary Computation Conference. ACM, New York, NY, 225–226. 10.1145/3319619.3322068 – reference: Nicola Beume, Boris Naujoks, and Michael Emmerich. 2007. SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research 181, 3 (2007), 1653–1669. – reference: Peng Yang, Ke Tang, and Xin Yao. 2018. Turning high-dimensional optimization into computationally expensive optimization. IEEE Transactions on Evolutionary Computation 22, 1 (2018), 143–156. – reference: Yin Zhang, Gai-Ge Wang, Keqin Li, Wei-Chang Yeh, Muwei Jian, and Junyu Dong. 2020. Enhancing MOEA/D with information feedback models for large-scale many-objective optimization. Information Sciences 522 (2020), 1–16. – reference: R. Eberhart and J. Kennedy. 1995. A new optimizer using particle swarm theory. In Proceedings of the 6th International Symposium on Micro Machine and Human Science. IEEE, Los Alamitos, CA, 39–43. – reference: Shan He, Guanbo Jia, Zexuan Zhu, Daniel A. Tennant, Qiang Huang, Ke Tang, Jing Liu, Mirco Musolesi, John K. Heath, and Xin Yao. 2016. Cooperative co-evolutionary module identification with application to cancer disease module discovery. IEEE Transactions on Evolutionary Computation 20, 6 (2016), 874–891. – reference: Clara Pizzuti and Annalisa Socievole. 2020. Multiobjective optimization and local merge for clustering attributed graphs. IEEE Transactions on Cybernetics 50, 12 (2020), 4997–5009. – reference: Heiner Zille, Hisao Ishibuchi, Sanaz Mostaghim, and Yusuke Nojima. 2018. A framework for large-scale multiobjective optimization based on problem transformation. IEEE Transactions on Evolutionary Computation 22, 2 (2018), 260–275. – reference: Abel García-Nájera and Antonio López-Jaimes. 2018. An investigation into many-objective optimization on combinatorial problems: Analyzing the pickup and delivery problem. Swarm and Evolutionary Computation 38 (2018), 218–230. – reference: Lei Zhang, Fengjiao Sun, Fan Cheng, Haiping Ma, and Xiaoyan Sun. An overlapping community detection based multi-objective evolutionary algorithm for diversified social influence maximization. In Proceedings of the 2020 IEEE Congress on Evolutionary Computation. IEEE, Los Alamitos, CA. – reference: Zhichao Lu, Ian Whalen, Vishnu Boddeti, Yashesh Dhebar, Kalyanmoy Deb, Erik Goodman, and Wolfgang Banzhaf. NSGA-Net: Neural architecture search using multiobjective genetic algorithm. In Proceedings of the 2019 Annual Genetic and Evolutionary Computation Conference. ACM, New York, NY. 10.1145/3321707.3321729 – reference: Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and T. Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 2 (2002), 182–197. 10.1109/4235.996017 – reference: Chunkai Zhang, Yepeng Deng, Xin Guo, Xuan Wang, and Chuanyi Liu. An adversarial attack based on multi-objective optimization in the black-box scenario: MOEA-APGA II. In Proceedings of the 2019 International Conference on Information and Communications Security. – reference: Kedar V. Khandeparkar, Shreevardhan A. Soman, and Gopal Gajjar. 2017. Detection and correction of systematic errors in instrument transformers along with line parameter estimation using PMU data. IEEE Transactions on Power Systems 32, 4 (2017), 3089–3098. – reference: Qiuzhen Lin, Jianqiang Li, Zhihua Du, Jianyong Chen, and Zhong Ming. 2015. A novel multi-objective particle swarm optimization with multiple search strategies. IEEE Transactions on Evolutionary Computation 247, 3 (2015), 732–744. – reference: Lyndon While, Philip Hingston, Luigi Barone, and Simon Huband. 2006. A faster algorithm for calculating hypervolume. IEEE Transactions on Evolutionary Computation 10, 1 (2006), 29–38. 10.1109/TEVC.2005.851275 – reference: Yu Wu, Yongshan Zhang, Xiaobo Liu, Zhihua Cai, and Yaoming Cai. 2018. A multiobjective optimization-based sparse extreme learning machine algorithm. Neurocomputing 317 (2018), 88–100. – reference: Qingfu Zhang, Aimin Zhou, and Yaochu Jin. 2008. RM-MEDA: A regularity model-based multiobjective estimation of distribution algorithm. IEEE Transactions on Evolutionary Computation 12, 1 (2008), 41–63. 10.1109/TEVC.2007.894202 – reference: Nicolas Jozefowiez, Fred Glover, and Manuel Laguna. 2008. Multi-objective meta-heuristics for the traveling salesman problem with profits. Journal of Mathematical Modelling and Algorithms 7 (2008), 177–195. – reference: Qingfu Zhang and Hui Li. 2007. MOEA/D: A multi-objective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation 11, 6 (2007), 712–731. 10.1109/TEVC.2007.892759 – reference: John H. Holland. 1992. Adaptation in Natural and Artificial Systems. MIT Press, Cambridge, MA. 10.5555/129194 – reference: Asja Fischer and Christian Igel. 2012. An introduction to restricted Boltzmann machines. In Proceedings of the Iberoamerican Congress on Pattern Recognition. 14–36. – reference: Zhaohui Yang, Yunhe Wang, Xinghao Chen, Boxin Shi, Chao Xu, Chunjing Xu, Qi Tian, and Chang Xu. CARS: Continuous evolution for efficient neural architecture search. In Proceedings of the 2020 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, Los Alamitos, CA. – reference: Yaochu Jin and J. Branke. 2005. Evolutionary optimization in uncertain environments—A survey. IEEE Transactions on Evolutionary Computation 9, 3 (2005), 303–317. 10.1109/TEVC.2005.846356 – reference: Aytug Onan, Serdar Korukoǧlu, and Hasan Bulut. 2016. A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification. Expert Systems with Applications 62 (2016), 1–16. 10.1016/j.eswa.2016.06.005 – reference: Seyedali Mirjalili. 2015. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems 89 (2015), 228–249. 10.1016/j.knosys.2015.07.006 – reference: Cheng He, Ran Cheng, and Danial Yazdani. 2020. Adaptive offspring generation for evolutionary large-scale multiobjective optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems. Early access, July 10, 2020. – reference: Xiaodong Li, Ke Tang, Mohammmad Nabi Omidvar, Zhenyu Yang, and Kai Qin. 2013. Benchmark Functions for the CEC’2013 Special Session and Competition on Large-Scale Global Optimization. Technical Report. RMIT University. – reference: Qingfu Zhang, Aimin Zhou, Shizheng Zhao, Ponnuthurai Nagaratnam Suganthan, Wudong Liu, and Santosh Tiwari. 2008. Multiobjective Optimization Test Instances for the CEC 2009 Special Session and Competition. Technical Report. University of Essex, Colchester, UK. – reference: Azadeh Mohammadi and Mohamad Saraee. 2018. Finding influential users for different time bounds in social networks using multi-objective optimization. Swarm and Evolutionary Computation 40 (2018), 158–165. – reference: Jiawei Su, Danilo Vasconcellos Vargas, and Kouichi Sakurai. 2019. One pixel attack for fooling deep neural networks. IEEE Transactions on Evolutionary Computation 23, 5 (2019), 828–841. – reference: J. Branke, B. Scheckenbach, M. Stein, K. Deb, and H. Schmeck. 2009. Portfolio optimization with an envelope-based multi-objective evolutionary algorithm. European Journal of Operational Research 199, 3 (2009), 684–693. – reference: Ye Tian, Chang Lu, Xingyi Zhang, Kay Chen Tan, and Yaochu Jin. 2021. Solving large-scale multiobjective optimization problems with sparse optimal solutions via unsupervised neural networks. IEEE Transactions on Cybernetics 51, 6 (2021), 3115–3128. – reference: Golnoosh Babaei and Shahrooz Bamdad. 2020. A multi-objective instance-based decision support system for investment recommendation in peer-to-peer lending. Expert Systems with Applications 150, 15 (2020), 113278. – volume-title: Proceedings of the 2013 IEEE Congress on Evolutionary Computation. IEEE ident: e_1_2_1_3_1 – ident: e_1_2_1_215_1 doi: 10.1016/j.ins.2020.02.066 – ident: e_1_2_1_24_1 doi: 10.1109/TCYB.2014.2322602 – ident: e_1_2_1_108_1 doi: 10.1145/3321707.3321729 – volume-title: Proceedings of the IEEE Congress on Evolutionary Computation. ident: e_1_2_1_143_1 – ident: e_1_2_1_213_1 doi: 10.1109/TEVC.2016.2600642 – ident: e_1_2_1_45_1 doi: 10.1145/3376916 – ident: e_1_2_1_117_1 doi: 10.1016/j.knosys.2015.12.022 – ident: e_1_2_1_87_1 doi: 10.1145/2792984 – ident: e_1_2_1_197_1 doi: 10.1002/int.4550080406 – ident: e_1_2_1_133_1 doi: 10.1109/TCYB.2018.2889413 – volume-title: Proceedings of the 2015 IEEE Symposium on Computational Intelligence and Data Mining. IEEE ident: e_1_2_1_131_1 – ident: e_1_2_1_123_1 doi: 10.1016/j.cie.2014.08.004 – ident: e_1_2_1_155_1 doi: 10.1109/TEVC.2018.2808689 – ident: e_1_2_1_107_1 doi: 10.1109/TEVC.2018.2879406 – volume-title: Proceedings of the 2019 IEEE Congress on Evolutionary Computation. IEEE ident: e_1_2_1_156_1 – ident: e_1_2_1_221_1 doi: 10.1109/SSCI.2016.7850214 – ident: e_1_2_1_71_1 doi: 10.1109/TEVC.2005.861417 – ident: e_1_2_1_181_1 doi: 10.1109/TEVC.2019.2950935 – ident: e_1_2_1_68_1 doi: 10.1109/TEVC.2018.2881153 – ident: e_1_2_1_190_1 doi: 10.1109/TSMCB.2012.2227469 – ident: e_1_2_1_15_1 doi: 10.1504/IJBIC.2016.076329 – ident: e_1_2_1_63_1 doi: 10.1109/TCYB.2020.2985081 – ident: e_1_2_1_165_1 doi: 10.1109/TCYB.2020.2979930 – ident: e_1_2_1_1_1 doi: 10.1016/j.asoc.2019.105991 – volume-title: Proceedings of the 6th International Symposium on Micro Machine and Human Science. IEEE ident: e_1_2_1_41_1 – ident: e_1_2_1_118_1 doi: 10.1016/j.advengsoft.2016.01.008 – ident: e_1_2_1_170_1 doi: 10.1109/TEVC.2020.3004012 – ident: e_1_2_1_67_1 doi: 10.5555/129194 – ident: e_1_2_1_168_1 doi: 10.1109/TFUZZ.2019.2945241 – ident: e_1_2_1_218_1 doi: 10.1016/j.petrol.2020.107192 – ident: e_1_2_1_121_1 doi: 10.1023/A:1006529012972 – volume-title: Proceedings of the 2020 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE ident: e_1_2_1_196_1 – ident: e_1_2_1_219_1 doi: 10.1016/j.swevo.2011.03.001 – ident: e_1_2_1_188_1 doi: 10.1155/2020/3106097 – ident: e_1_2_1_152_1 doi: 10.1145/2001576.2001781 – ident: e_1_2_1_225_1 doi: 10.1007/978-3-030-12598-1_32 – volume: 26 start-page: 30 year: 1996 ident: e_1_2_1_32_1 article-title: A combined genetic adaptive search (GeneAS) for engineering design publication-title: Computer Science and Informatics – ident: e_1_2_1_142_1 doi: 10.1016/j.asoc.2010.11.007 – ident: e_1_2_1_51_1 doi: 10.1109/TEVC.2017.2726341 – ident: e_1_2_1_30_1 doi: 10.1016/j.knosys.2016.05.033 – ident: e_1_2_1_94_1 doi: 10.1145/3205651.3208250 – ident: e_1_2_1_126_1 doi: 10.1109/TEVC.2013.2281543 – ident: e_1_2_1_27_1 doi: 10.1016/j.cor.2013.11.014 – ident: e_1_2_1_49_1 doi: 10.1007/978-3-642-33275-3_2 – ident: e_1_2_1_194_1 doi: 10.1109/TNNLS.2021.3061630 – ident: e_1_2_1_92_1 doi: 10.1109/TEVC.2018.2855411 – ident: e_1_2_1_207_1 doi: 10.1109/TBDATA.2020.2993446 – ident: e_1_2_1_211_1 doi: 10.1109/TEVC.2007.894202 – ident: e_1_2_1_130_1 doi: 10.1016/j.ins.2015.06.044 – ident: e_1_2_1_154_1 doi: 10.1109/TEVC.2018.2882166 – ident: e_1_2_1_10_1 doi: 10.1007/978-3-642-25832-9_20 – ident: e_1_2_1_179_1 doi: 10.1109/TCYB.2018.2821180 – ident: e_1_2_1_114_1 doi: 10.1038/s41598-019-45814-8 – ident: e_1_2_1_193_1 doi: 10.1109/TEVC.2012.2227145 – volume-title: Zhenyu Yang, and Kai Qin. year: 2013 ident: e_1_2_1_95_1 – volume-title: Proceedings of the 7th International Conference on Learning Representations. ident: e_1_2_1_102_1 – volume-title: Proceedings of the 2019 IEEE International Conference on Data Science in Cyberspace. IEEE ident: e_1_2_1_37_1 – volume-title: Proceedings of the 2019 IEEE Congress on Evolutionary Computation. IEEE ident: e_1_2_1_169_1 – ident: e_1_2_1_46_1 doi: 10.1109/CEC.2017.7969315 – ident: e_1_2_1_224_1 doi: 10.1109/SSCI.2017.8280974 – volume: 9 start-page: 115 year: 1995 ident: e_1_2_1_31_1 article-title: Simulated binary crossover for continuous search space publication-title: Complex Systems – volume-title: Proceedings of the 2019 International Conference on Information and Communications Security. ident: e_1_2_1_203_1 – ident: e_1_2_1_217_1 doi: 10.1145/2939672.2939861 – ident: e_1_2_1_129_1 doi: 10.1109/TGRS.2009.2023666 – ident: e_1_2_1_11_1 doi: 10.1007/978-3-319-54157-0_4 – volume-title: Artificial Intelligence in the Age of Neural Networks and Brain Computing ident: e_1_2_1_115_1 – ident: e_1_2_1_104_1 doi: 10.1109/TNNLS.2017.2695223 – ident: e_1_2_1_28_1 doi: 10.1007/s00500-017-2965-0 – volume: 111 start-page: 203 year: 2014 ident: e_1_2_1_56_1 article-title: The multi-objective multi-vehicle pickup and delivery problem with time windows. Procedia publication-title: Social and Behavioral Sciences – ident: e_1_2_1_199_1 doi: 10.1016/j.ins.2018.10.005 – volume-title: Multiobjective Optimization Test Instances for the CEC 2009 Special Session and Competition. Technical Report year: 2008 ident: e_1_2_1_128_1 – ident: e_1_2_1_212_1 doi: 10.1109/MCI.2017.2708578 – ident: e_1_2_1_88_1 – ident: e_1_2_1_175_1 doi: 10.1145/1390156.1390294 – ident: e_1_2_1_180_1 doi: 10.1109/TCYB.2015.2409837 – ident: e_1_2_1_158_1 doi: 10.1109/CEC.2013.6557555 – ident: e_1_2_1_33_1 doi: 10.1109/TEVC.2013.2281535 – ident: e_1_2_1_172_1 doi: 10.1109/TCYB.2019.2906383 – volume-title: Proceedings of the 5th International Conference on Intelligent Data Engineering and Automated Learning. ident: e_1_2_1_48_1 – ident: e_1_2_1_26_1 doi: 10.1109/TCYB.2016.2600577 – ident: e_1_2_1_186_1 doi: 10.1109/TII.2019.2962137 – ident: e_1_2_1_16_1 doi: 10.1109/TEVC.2014.2350995 – ident: e_1_2_1_184_1 doi: 10.1016/j.asoc.2014.08.036 – ident: e_1_2_1_34_1 doi: 10.1109/4235.996017 – ident: e_1_2_1_40_1 doi: 10.1109/TII.2018.2836189 – ident: e_1_2_1_86_1 doi: 10.1016/j.cosrev.2018.02.002 – ident: e_1_2_1_214_1 doi: 10.1109/TCYB.2018.2871673 – ident: e_1_2_1_110_1 doi: 10.1109/TEVC.2015.2455812 – ident: e_1_2_1_192_1 doi: 10.1109/TEVC.2017.2672689 – ident: e_1_2_1_53_1 doi: 10.1109/TEVC.2013.2260862 – ident: e_1_2_1_148_1 doi: 10.1109/CEC.2016.7743831 – ident: e_1_2_1_90_1 doi: 10.1109/TNNLS.2017.2677973 – ident: e_1_2_1_111_1 doi: 10.1007/s10852-007-9073-6 – ident: e_1_2_1_2_1 doi: 10.2307/1910081 – ident: e_1_2_1_29_1 doi: 10.1109/CEC.2017.7969486 – ident: e_1_2_1_76_1 doi: 10.1109/TEVC.2005.846356 – ident: e_1_2_1_139_1 doi: 10.1109/ACCESS.2020.2980942 – ident: e_1_2_1_109_1 doi: 10.1007/s10732-009-9103-9 – ident: e_1_2_1_145_1 doi: 10.1109/TEVC.2017.2782571 – ident: e_1_2_1_113_1 doi: 10.1016/j.ejor.2013.09.008 – ident: e_1_2_1_191_1 doi: 10.1038/s41598-017-12773-x – ident: e_1_2_1_140_1 doi: 10.1214/aoms/1177729586 – ident: e_1_2_1_149_1 doi: 10.1023/A:1008202821328 – ident: e_1_2_1_210_1 doi: 10.1109/TEVC.2007.892759 – ident: e_1_2_1_5_1 doi: 10.1145/3319619.3322068 – ident: e_1_2_1_42_1 doi: 10.5555/1121732 – ident: e_1_2_1_204_1 doi: 10.1016/j.asoc.2017.09.033 – ident: e_1_2_1_75_1 doi: 10.1007/s13042-019-01030-4 – ident: e_1_2_1_182_1 doi: 10.1016/j.eswa.2018.03.018 – volume: 189 start-page: 33 year: 2015 ident: e_1_2_1_72_1 article-title: Multi-objective optimization model for a green vehicle routing problem. Procedia publication-title: Social and Behavioral Sciences – volume-title: Proceedings of the 2015 European Conference on the Applications of Evolutionary Computation. ident: e_1_2_1_173_1 – ident: e_1_2_1_60_1 doi: 10.1109/CEC48606.2020.9185553 – volume-title: Proceedings of the 2004 Genetic and Evolutionary Computation Conference. ident: e_1_2_1_150_1 – ident: e_1_2_1_61_1 doi: 10.1109/TSMC.2020.3003926 – ident: e_1_2_1_138_1 doi: 10.1038/s41598-017-00090-2 – volume-title: Proceedings of the 1998 IEEE International Conference on Evolutionary Computation. IEEE ident: e_1_2_1_146_1 – ident: e_1_2_1_38_1 doi: 10.15837/ijccc.2011.2.2172 – volume-title: Kingma and Jimmy Ba year: 2014 ident: e_1_2_1_84_1 – ident: e_1_2_1_58_1 doi: 10.1109/TEVC.2006.877146 – ident: e_1_2_1_216_1 doi: 10.1145/3078848 – ident: e_1_2_1_6_1 doi: 10.1016/j.eswa.2020.113278 – ident: e_1_2_1_141_1 doi: 10.1109/TEVC.2017.2688863 – ident: e_1_2_1_209_1 doi: 10.1016/j.engappai.2018.09.009 – ident: e_1_2_1_136_1 doi: 10.5555/3298239.3298367 – ident: e_1_2_1_200_1 doi: 10.1109/TEVC.2017.2754271 – volume-title: Goodman year: 2019 ident: e_1_2_1_47_1 – volume-title: Evolutionary Multiobjective Optimization ident: e_1_2_1_35_1 – ident: e_1_2_1_161_1 doi: 10.1109/MCI.2017.2742868 – ident: e_1_2_1_78_1 doi: 10.1007/s10852-008-9080-2 – volume-title: Proceedings of the 2017 European Conference on the Applications of Evolutionary Computation. ident: e_1_2_1_13_1 – ident: e_1_2_1_12_1 doi: 10.1016/j.ejor.2008.01.054 – ident: e_1_2_1_171_1 doi: 10.1109/TEVC.2019.2918140 – ident: e_1_2_1_50_1 doi: 10.1016/j.swevo.2017.08.001 – ident: e_1_2_1_106_1 doi: 10.1016/j.asoc.2020.106120 – ident: e_1_2_1_125_1 doi: 10.1016/j.eswa.2004.10.014 – ident: e_1_2_1_70_1 doi: 10.1109/TEVC.2020.2987804 – ident: e_1_2_1_103_1 doi: 10.1186/s12859-017-1657-1 – ident: e_1_2_1_144_1 doi: 10.1145/3205455.3205491 – ident: e_1_2_1_183_1 doi: 10.1109/TEVC.2005.851275 – volume-title: Proceedings of the 5th International Conference on Optimization: Techniques and Applications. ident: e_1_2_1_100_1 – ident: e_1_2_1_195_1 doi: 10.1016/j.ins.2008.02.017 – ident: e_1_2_1_167_1 doi: 10.1109/TETCI.2018.2872055 – ident: e_1_2_1_159_1 doi: 10.1109/TEVC.2019.2909744 – ident: e_1_2_1_124_1 doi: 10.1109/TEVC.2019.2913831 – ident: e_1_2_1_7_1 doi: 10.1016/j.asoc.2018.07.014 – ident: e_1_2_1_59_1 doi: 10.1162/106365601750190398 – volume: 5 start-page: 307 year: 2017 ident: e_1_2_1_44_1 article-title: A multi-objective approach to fuzzy clustering using ITLBO algorithm publication-title: Journal of AI and Data Mining – volume-title: Proceedings of the 2020 IEEE Congress on Evolutionary Computation. IEEE ident: e_1_2_1_206_1 – ident: e_1_2_1_19_1 doi: 10.1016/j.ins.2018.10.007 – ident: e_1_2_1_119_1 doi: 10.1016/j.swevo.2018.02.003 – ident: e_1_2_1_185_1 doi: 10.1016/j.neucom.2018.07.060 – volume: 247 start-page: 732 year: 2015 ident: e_1_2_1_101_1 article-title: A novel multi-objective particle swarm optimization with multiple search strategies publication-title: IEEE Transactions on Evolutionary Computation – ident: e_1_2_1_105_1 doi: 10.1016/j.swevo.2020.100684 – volume: 80 start-page: 4771 year: 2018 ident: e_1_2_1_153_1 article-title: Exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search publication-title: Proceedings of Machine Learning Research – ident: e_1_2_1_80_1 doi: 10.1016/j.ejor.2007.05.055 – ident: e_1_2_1_132_1 doi: 10.1109/TEVC.2011.2161090 – ident: e_1_2_1_43_1 doi: 10.1109/TEVC.2005.859463 – ident: e_1_2_1_208_1 doi: 10.1109/TNSE.2020.2972980 – ident: e_1_2_1_83_1 doi: 10.21437/Interspeech.2019-2420 – ident: e_1_2_1_198_1 doi: 10.1109/5.784219 – ident: e_1_2_1_202_1 doi: 10.1109/TEVC.2018.2836912 – ident: e_1_2_1_134_1 doi: 10.1016/j.cor.2015.04.009 – ident: e_1_2_1_137_1 doi: 10.1109/TEVC.2021.3063606 – ident: e_1_2_1_66_1 doi: 10.1145/2517649 – ident: e_1_2_1_98_1 doi: 10.1155/2018/2613739 – ident: e_1_2_1_25_1 doi: 10.1109/TEVC.2015.2395073 – year: 2020 ident: e_1_2_1_166_1 article-title: EMODMI: A multi-objective optimization based method to identify disease modules publication-title: IEEE Transactions on Emerging Topics in Computational Intelligence. Early access – ident: e_1_2_1_52_1 doi: 10.1073/pnas.122653799 – ident: e_1_2_1_223_1 doi: 10.1109/TEVC.2017.2704782 – ident: e_1_2_1_187_1 doi: 10.1145/3176644 – ident: e_1_2_1_79_1 doi: 10.5555/645826.669582 – ident: e_1_2_1_127_1 doi: 10.1016/j.eswa.2016.06.005 – ident: e_1_2_1_17_1 doi: 10.1016/j.swevo.2019.100626 – volume: 7 start-page: 77 year: 1952 ident: e_1_2_1_112_1 article-title: Portfolio selection publication-title: Journal of Finance – volume-title: Proceedings of the International Conference on Parallel Problem Solving from Nature. 525–534 ident: e_1_2_1_4_1 – volume-title: Proceedings of the 2019 IEEE Congress on Evolutionary Computation. IEEE ident: e_1_2_1_201_1 – ident: e_1_2_1_36_1 doi: 10.1016/j.ejor.2006.06.042 – year: 2020 ident: e_1_2_1_164_1 article-title: A pattern mining based evolutionary algorithm for large-scale sparse multi-objective optimization problems publication-title: IEEE Transactions on Cybernetics. Early access – ident: e_1_2_1_14_1 doi: 10.1145/2666003 – ident: e_1_2_1_57_1 doi: 10.1007/s10462-019-09800-w – ident: e_1_2_1_96_1 doi: 10.1007/s10732-015-9289-y – ident: e_1_2_1_135_1 doi: 10.5555/2888116.2888125 – ident: e_1_2_1_174_1 doi: 10.5555/2873822.2873894 – ident: e_1_2_1_81_1 doi: 10.1145/956750.956769 – ident: e_1_2_1_205_1 doi: 10.1109/TCYB.2017.2711038 – ident: e_1_2_1_39_1 doi: 10.1145/3205651.3208243 – ident: e_1_2_1_8_1 doi: 10.5555/3305381.3305429 – ident: e_1_2_1_176_1 doi: 10.1145/3321707.3321735 – ident: e_1_2_1_189_1 doi: 10.1109/TEVC.2020.3016049 – ident: e_1_2_1_69_1 doi: 10.1016/j.asoc.2014.08.024 – ident: e_1_2_1_89_1 doi: 10.1109/TCYB.2015.2507366 – ident: e_1_2_1_82_1 doi: 10.1109/TPWRS.2016.2620990 – ident: e_1_2_1_62_1 doi: 10.1109/TEVC.2020.2967501 – ident: e_1_2_1_21_1 doi: 10.5555/1887255.1887289 – ident: e_1_2_1_91_1 doi: 10.1145/3136625 – ident: e_1_2_1_116_1 doi: 10.1016/j.knosys.2015.07.006 – ident: e_1_2_1_54_1 doi: 10.1109/TNNLS.2015.2469673 – ident: e_1_2_1_163_1 doi: 10.1109/TEVC.2020.3044711 – ident: e_1_2_1_151_1 doi: 10.1109/TEVC.2019.2890858 – ident: e_1_2_1_20_1 doi: 10.1109/ICDCS.2018.00031 – ident: e_1_2_1_120_1 doi: 10.1007/s00500-018-3269-8 – ident: e_1_2_1_18_1 doi: 10.1109/TII.2017.2676000 – ident: e_1_2_1_64_1 doi: 10.1109/TEVC.2019.2896002 – volume: 744 volume-title: Nature-Inspired Algorithms and Applied Optimization. Studies in Computational Intelligence ident: e_1_2_1_74_1 – volume: 20 start-page: 874 year: 2016 ident: e_1_2_1_65_1 article-title: Cooperative co-evolutionary module identification with application to cancer disease module discovery publication-title: IEEE Transactions on Evolutionary Computation – ident: e_1_2_1_73_1 doi: 10.1145/331499.331504 – ident: e_1_2_1_97_1 doi: 10.1109/TCYB.2017.2720180 – ident: e_1_2_1_22_1 doi: 10.1016/j.neucom.2020.02.028 – ident: e_1_2_1_93_1 doi: 10.1109/TCYB.2020.2977661 – ident: e_1_2_1_99_1 – ident: e_1_2_1_9_1 doi: 10.1016/j.ejor.2006.08.008 – ident: e_1_2_1_55_1 doi: 10.5555/2969033.2969125 – ident: e_1_2_1_160_1 doi: 10.1145/2396761.2396775 – ident: e_1_2_1_147_1 doi: 10.1137/1.9781611972795.100 – ident: e_1_2_1_177_1 doi: 10.1109/TSMC.2018.2861879 – ident: e_1_2_1_178_1 doi: 10.1016/j.neucom.2020.01.114 – volume-title: A multistage evolutionary algorithm for better diversity preservation in multiobjective optimization year: 2019 ident: e_1_2_1_162_1 – ident: e_1_2_1_77_1 doi: 10.1109/TSMCC.2008.919172 – ident: e_1_2_1_226_1 doi: 10.1162/106365600568202 – ident: e_1_2_1_222_1 doi: 10.1145/2908961.2908979 – ident: e_1_2_1_85_1 doi: 10.1109/CEC.2005.1554717 – ident: e_1_2_1_157_1 doi: 10.1007/s10589-005-3070-3 – ident: e_1_2_1_122_1 doi: 10.1109/MCDM.2009.4938830 |
| SSID | ssj0002416 |
| Score | 2.711824 |
| SecondaryResourceType | review_article |
| Snippet | Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in solving various optimization problems, but their performance may... |
| SourceID | proquest crossref acm |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1 |
| SubjectTerms | Computer science Computing methodologies Evolutionary algorithms General and reference Genetic algorithms Multiple objective analysis Optimization Performance assessment Surveys and overviews Theory of computation Theory of randomized search heuristics |
| SubjectTermsDisplay | Computing methodologies -- Genetic algorithms General and reference -- Surveys and overviews Theory of computation -- Theory of randomized search heuristics |
| Title | Evolutionary Large-Scale Multi-Objective Optimization: A Survey |
| URI | https://dl.acm.org/doi/10.1145/3470971 https://www.proquest.com/docview/2717339262 |
| Volume | 54 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3PT9swFLYYXLiMjR8aUCYfJi7IkMSOk-yCIsaEJgrS2knlFMWuA0yjm6CtxP76Pccvbrr2sHGJItuxkvc9f3523nsm5ANPeBgpEbEyBAoUpchYmsUwrrJwqAMNSlLaQOHulbz4Jr4M4kFzJDxGl4zVsf69NK7kJahCGeBqo2T_A1nfKRTAPeALV0AYrv-E8fkUu7eub5fWp5v1QOYwUq2bILtW3x2dHV0DMTxgxKWLRe9NHqfzv3Tzs27tYD6pHaGf6npvcPfv3UbpjdeDHoZVj2713cRrmN9_HkAnz_cz5wHjir-iMuI-AyxRMb-h14xui5q4DBjQg6s2SJ1xwhLu0lg13OoSRKMOpS2iDFszrtvNXORyYdNecJHYNFez6co7EWLNK7IWwRIBSHkt_9S97Pl5GGwT_FPt3tWFTNtuT_BRa5Hoh3mLZH5Crq2M_hvyGpcHNHdYvyUrZrRJNpqjNygy8RY5bUNPW9DTv6Cnbeg_0pw64LdJ__N5_-yC4VkYrOQ8HTMhpeJhBeZlHEojVGmiWGdggXCZhDGMqVIEwig5tIHSAay54ftNlATSpJnUMd8hq6OfI_OO0Co1GdjNOqoqIYbSlGFisjIRKtUKrFm9SzZBKMUvl-ykQFHtksNGSIXG7PH2EJMfhYtsj2cNqW_Y9LHQpNNIucDR9FRE1h2E2-yVe0tfYJ-sz7SyQ1bHjxNzAPbgWL1H4P8A54ZbjQ |
| linkProvider | EBSCOhost |
| 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=Evolutionary+Large-Scale+Multi-Objective+Optimization%3A+A+Survey&rft.jtitle=ACM+computing+surveys&rft.au=Tian%2C+Ye&rft.au=Si%2C+Langchun&rft.au=Zhang%2C+Xingyi&rft.au=Cheng%2C+Ran&rft.date=2022-11-30&rft.pub=ACM&rft.issn=0360-0300&rft.eissn=1557-7341&rft.volume=54&rft.issue=8&rft.spage=1&rft.epage=34&rft_id=info:doi/10.1145%2F3470971&rft.externalDocID=3470971 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0360-0300&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0360-0300&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0360-0300&client=summon |