A Review on Evolutionary Multitask Optimization: Trends and Challenges

Evolutionary algorithms (EAs) possess strong problem-solving abilities and have been applied in a wide range of applications. However, they still suffer from a high computational burden and poor generalization ability. To overcome the limitations, numerous studies consider conducting knowledge extra...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 26; H. 5; S. 941 - 960
Hauptverfasser:	Wei, Tingyang, Wang, Shibin, Zhong, Jinghui, Liu, Dong, Zhang, Jun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Costs Evolutionary algorithm (EA) Evolutionary algorithms Evolutionary computation evolutionary multitasking Multitasking Optimization Sociology Statistics Task analysis transfer optimization Trends
ISSN:	1089-778X, 1941-0026
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Abstract	Evolutionary algorithms (EAs) possess strong problem-solving abilities and have been applied in a wide range of applications. However, they still suffer from a high computational burden and poor generalization ability. To overcome the limitations, numerous studies consider conducting knowledge extraction across distinct optimization task domains. Among these research strands, one representative tributary is evolutionary multitask optimization (EMTO) that aims to resolve multiple optimization tasks simultaneously. The underlying attribute of implicit parallelism for EAs can well incorporate with the framework of EMTO, giving rise to the ascending EMTO studies. This review is intended to present a detailed exposition on the research in the EMTO area. We reveal the core components for designing the EMTO algorithms. Subsequently, we organize the works lying in the fusions between EMTO and traditional EAs. By analyzing the associations for diverse strategies in different branches of EMTO, this review uncovers the research trends and the potentially important directions, with additional interesting real-world applications mentioned.
AbstractList	Evolutionary algorithms (EAs) possess strong problem-solving abilities and have been applied in a wide range of applications. However, they still suffer from a high computational burden and poor generalization ability. To overcome the limitations, numerous studies consider conducting knowledge extraction across distinct optimization task domains. Among these research strands, one representative tributary is evolutionary multitask optimization (EMTO) that aims to resolve multiple optimization tasks simultaneously. The underlying attribute of implicit parallelism for EAs can well incorporate with the framework of EMTO, giving rise to the ascending EMTO studies. This review is intended to present a detailed exposition on the research in the EMTO area. We reveal the core components for designing the EMTO algorithms. Subsequently, we organize the works lying in the fusions between EMTO and traditional EAs. By analyzing the associations for diverse strategies in different branches of EMTO, this review uncovers the research trends and the potentially important directions, with additional interesting real-world applications mentioned.
Author	Zhong, Jinghui Wei, Tingyang Wang, Shibin Liu, Dong Zhang, Jun
Author_xml	– sequence: 1 givenname: Tingyang surname: Wei fullname: Wei, Tingyang email: dusting.way@foxmail.com organization: School of Computer Science and Engineering, South China University of Technology, Guangzhou, China – sequence: 2 givenname: Shibin surname: Wang fullname: Wang, Shibin email: wangshibin@htu.edu.cn organization: College of Computer and Information Engineering, Henan Normal University, Xinxiang, China – sequence: 3 givenname: Jinghui orcidid: 0000-0003-0113-3430 surname: Zhong fullname: Zhong, Jinghui email: jinghuizhong@scut.edu.cn organization: School of Computer Science and Engineering, South China University of Technology, Guangzhou, China – sequence: 4 givenname: Dong orcidid: 0000-0003-4346-9565 surname: Liu fullname: Liu, Dong email: liudong@htu.edu.cn organization: College of Computer and Information Engineering, Henan Normal University, Xinxiang, China – sequence: 5 givenname: Jun orcidid: 0000-0001-7835-9871 surname: Zhang fullname: Zhang, Jun email: junzhang@ieee.org organization: Hanyang University, Ansan, South Korea
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Cites_doi	10.1007/s12293-021-00331-y 10.1109/CEC.2018.8477722 10.24963/ijcai.2018/538 10.1016/j.ins.2021.06.056 10.1109/TEVC.2009.2027359 10.5591/978-1-57735-516-8/IJCAI11-259 10.1109/TEVC.2019.2962747 10.1109/TEVC.2019.2894743 10.1109/CEC.2019.8790024 10.1109/TEVC.2019.2926107 10.1109/TENCON.2016.7848632 10.1109/CEC.2019.8790143 10.1109/TEVC.2018.2855411 10.1007/978-3-030-03991-2_66 10.1016/j.knosys.2019.105294 10.1609/aaai.v33i01.33014295 10.1109/TCYB.2021.3052509 10.1109/TCYB.2020.3036393 10.1109/MCI.2021.3108310 10.1109/TCYB.2020.2989465 10.1109/TEVC.2020.3048174 10.1016/j.ins.2019.09.058 10.1109/TETCI.2017.2769104 10.1109/TCYB.2020.2981733 10.1109/TCYB.2020.2969025 10.1109/CEC48606.2020.9185667 10.3390/math9080864 10.1109/TEVC.2020.2991717 10.1109/TETCI.2019.2916051 10.1109/TEVC.2021.3051608 10.1007/978-3-319-68759-9_38 10.2991/ijcis.d.191004.001 10.1007/BFb0029746 10.1109/CEC.2016.7743992 10.1109/TEVC.2019.2893614 10.1109/CVPR.2017.328 10.1145/3205651.3208228 10.1007/s12559-020-09777-7 10.1109/TCYB.2016.2554622 10.1109/TEVC.2019.2906927 10.1109/TEVC.2021.3065707 10.1109/TEVC.2021.3107435 10.1016/j.asoc.2007.07.010 10.1109/TCYB.2020.3029176 10.1109/TKDE.2021.3070203 10.1016/j.asoc.2019.105631 10.5555/2946645.2946704 10.1016/j.swevo.2012.05.001 10.1016/j.knosys.2020.106262 10.3389/fnins.2019.01396 10.1109/TEVC.2021.3083362 10.1109/TEVC.2015.2458037 10.1109/TEVC.2018.2868770 10.1145/3205651.3205736 10.1109/TEVC.2017.2657556 10.1109/CEC.2018.8477830 10.1007/s12559-022-10012-8 10.1109/TCYB.2018.2864345 10.1109/TEVC.2017.2783441 10.1109/TPAMI.2021.3057446 10.1109/TCYB.2021.3050516 10.1109/TEVC.2019.2904696 10.1109/CEC.2017.7969454 10.1109/CVPR.2012.6247911 10.1109/TPAMI.2021.3054719 10.1016/j.eswa.2019.07.015 10.1145/3377929.3398084 10.1109/ICCV.2013.368 10.1007/s10994-009-5152-4 10.1109/4235.996017 10.1109/SSCI.2016.7850040 10.1109/CEC.2009.4982949 10.1109/CEC48606.2020.9185660 10.1145/3205651.3205761 10.1109/TSMC.2018.2853719 10.1109/TEVC.2020.3004027 10.1109/TCYB.2020.2980888 10.1007/978-3-319-94472-2_10 10.1109/BigData47090.2019.9006274 10.1145/3341069.3342982 10.1109/CEC.2019.8789940 10.1007/978-981-15-3425-6_2 10.1109/TEVC.2021.3086308 10.1109/TCYB.2020.3017017 10.1109/CVPR.2017.316 10.1109/TCYB.2018.2845361 10.1007/s12293-021-00346-5 10.1007/s40747-016-0011-y 10.1109/SSCI.2016.7850039 10.1109/TEVC.2021.3097043 10.1109/TKDE.2009.191 10.1109/TEVC.2018.2881955 10.1109/CEC.2019.8790352 10.1007/978-3-030-58115-2_44 10.1109/CEC.2016.7744178 10.1109/CVPR.2019.00283 10.1109/TEVC.2020.3023480 10.1109/SSCI.2016.7850038 10.1109/TEVC.2021.3110506 10.1109/CEC48606.2020.9185907 10.1109/TEVC.2017.2785351 10.1007/978-3-642-12683-3_34 10.1145/3386252 10.1109/CEC.2017.7969596 10.1109/TCYB.2020.3043509 10.1109/TEVC.2020.3044711 10.1007/s12559-016-9395-7 10.1109/TETC.2019.2945775 10.1109/CEC.2019.8790034 10.1109/TSC.2018.2827070 10.1109/TCYB.2019.2955599 10.1109/TEVC.2007.892759 10.1109/TCYB.2020.2974100 10.1145/3377929.3389934 10.1109/TEVC.2019.2955110 10.1109/TEVC.2014.2308294 10.1109/TCYB.2019.2962865 10.1016/j.engappai.2017.05.008 10.1109/MCI.2020.3039066 10.1109/TFUZZ.2020.2968863 10.1109/TCYB.2020.3013271 10.1109/CEC.2019.8789909 10.1109/CEC.2018.8477860 10.1109/CDC.2017.8263646
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References	ref57 ref56 ref59 ref58 ref53 Yuan (ref128) 2017 ref52 ref55 ref54 ref51 ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 ref49 ref7 ref9 ref4 ref3 ref6 ref5 ref100 ref101 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref32 ref39 ref38 ref24 Xu (ref14) 2021; 9 ref23 Tam (ref95) 2021; 576 ref26 ref25 ref20 ref22 Chen (ref8) 2021 ref21 ref28 ref27 ref29 ref13 ref12 ref15 ref129 ref97 ref126 ref96 ref127 ref11 ref99 ref124 ref10 ref98 ref125 ref17 ref16 ref19 ref18 ref93 ref92 ref94 ref91 ref90 ref89 ref86 ref85 ref88 ref87 ref82 ref81 ref84 ref83 ref80 ref79 ref108 ref78 ref109 ref106 ref107 ref75 ref104 ref74 ref105 ref77 ref102 ref76 ref103 ref2 Hoffman (ref114) ref1 ref71 ref111 ref70 ref112 ref73 ref72 ref110 ref68 ref119 ref67 ref117 ref69 ref118 ref64 ref115 ref63 ref116 ref66 ref113 ref65 Liang (ref50) 2020 ref60 ref122 ref123 ref62 ref120 ref61 ref121 Da (ref33) 2017
References_xml	– ident: ref72 doi: 10.1007/s12293-021-00331-y – ident: ref20 doi: 10.1109/CEC.2018.8477722 – ident: ref31 doi: 10.24963/ijcai.2018/538 – volume: 576 start-page: 355 year: 2021 ident: ref95 article-title: Multifactorial evolutionary optimization to maximize lifetime of wireless sensor network publication-title: Inf. Sci. doi: 10.1016/j.ins.2021.06.056 – ident: ref68 doi: 10.1109/TEVC.2009.2027359 – ident: ref105 doi: 10.5591/978-1-57735-516-8/IJCAI11-259 – ident: ref65 doi: 10.1109/TEVC.2019.2962747 – ident: ref124 doi: 10.1109/TEVC.2019.2894743 – ident: ref101 doi: 10.1109/CEC.2019.8790024 – ident: ref30 doi: 10.1109/TEVC.2019.2926107 – ident: ref73 doi: 10.1109/TENCON.2016.7848632 – ident: ref79 doi: 10.1109/CEC.2019.8790143 – ident: ref125 doi: 10.1109/TEVC.2018.2855411 – ident: ref77 doi: 10.1007/978-3-030-03991-2_66 – ident: ref85 doi: 10.1016/j.knosys.2019.105294 – ident: ref37 doi: 10.1609/aaai.v33i01.33014295 – ident: ref120 doi: 10.1109/TCYB.2021.3052509 – ident: ref55 doi: 10.1109/TCYB.2020.3036393 – ident: ref78 doi: 10.1109/MCI.2021.3108310 – ident: ref118 doi: 10.1109/TCYB.2020.2989465 – year: 2017 ident: ref128 article-title: Evolutionary multitasking for multiobjective continuous optimization: Benchmark problems, performance metrics and baseline results publication-title: arXiv:1706.02766 – ident: ref4 doi: 10.1109/TEVC.2020.3048174 – year: 2020 ident: ref50 article-title: A two stage adaptive knowledge transfer evolutionary multi-tasking based on population distribution for multi/many-objective optimization publication-title: arXiv:2001.00810 – ident: ref66 doi: 10.1016/j.ins.2019.09.058 – ident: ref10 doi: 10.1109/TETCI.2017.2769104 – ident: ref60 doi: 10.1109/TCYB.2020.2981733 – ident: ref63 doi: 10.1109/TCYB.2020.2969025 – ident: ref88 doi: 10.1109/CEC48606.2020.9185667 – volume: 9 start-page: 864 issue: 8 year: 2021 ident: ref14 article-title: Multi-task optimization and multi-task evolutionary computation in the past five years: A brief review publication-title: Mathematics doi: 10.3390/math9080864 – ident: ref98 doi: 10.1109/TEVC.2020.2991717 – ident: ref17 doi: 10.1109/TETCI.2019.2916051 – ident: ref2 doi: 10.1109/TEVC.2021.3051608 – ident: ref81 doi: 10.1007/978-3-319-68759-9_38 – ident: ref25 doi: 10.2991/ijcis.d.191004.001 – ident: ref27 doi: 10.1007/BFb0029746 – ident: ref70 doi: 10.1109/CEC.2016.7743992 – ident: ref21 doi: 10.1109/TEVC.2019.2893614 – ident: ref110 doi: 10.1109/CVPR.2017.328 – ident: ref24 doi: 10.1145/3205651.3208228 – ident: ref53 doi: 10.1007/s12559-020-09777-7 – ident: ref59 doi: 10.1109/TCYB.2016.2554622 – ident: ref38 doi: 10.1109/TEVC.2019.2906927 – ident: ref96 doi: 10.1109/TEVC.2021.3065707 – ident: ref83 doi: 10.1109/TEVC.2021.3107435 – ident: ref100 doi: 10.1016/j.asoc.2007.07.010 – ident: ref58 doi: 10.1109/TCYB.2020.3029176 – ident: ref6 doi: 10.1109/TKDE.2021.3070203 – ident: ref123 doi: 10.1016/j.asoc.2019.105631 – ident: ref112 doi: 10.5555/2946645.2946704 – ident: ref119 doi: 10.1016/j.swevo.2012.05.001 – year: 2021 ident: ref8 article-title: Multi-task learning in natural language processing: An overview publication-title: arXiv:2109.09138 – ident: ref69 doi: 10.1016/j.knosys.2020.106262 – ident: ref22 doi: 10.3389/fnins.2019.01396 – ident: ref89 doi: 10.1109/TEVC.2021.3083362 – ident: ref15 doi: 10.1109/TEVC.2015.2458037 – ident: ref28 doi: 10.1109/TEVC.2018.2868770 – ident: ref32 doi: 10.1145/3205651.3205736 – ident: ref90 doi: 10.1109/TEVC.2017.2657556 – ident: ref19 doi: 10.1109/CEC.2018.8477830 – ident: ref13 doi: 10.1007/s12559-022-10012-8 – ident: ref35 doi: 10.1109/TCYB.2018.2864345 – ident: ref36 doi: 10.1109/TEVC.2017.2783441 – ident: ref111 doi: 10.1109/TPAMI.2021.3057446 – start-page: 1989 volume-title: Proc. 35th Int. Conf. Mach. Learn. ident: ref114 article-title: CyCADA: Cycle-consistent adversarial domain adaptation – ident: ref71 doi: 10.1109/TCYB.2021.3050516 – ident: ref44 doi: 10.1109/TEVC.2019.2904696 – ident: ref51 doi: 10.1109/CEC.2017.7969454 – ident: ref107 doi: 10.1109/CVPR.2012.6247911 – ident: ref7 doi: 10.1109/TPAMI.2021.3054719 – ident: ref48 doi: 10.1016/j.eswa.2019.07.015 – ident: ref42 doi: 10.1145/3377929.3398084 – ident: ref104 doi: 10.1109/ICCV.2013.368 – ident: ref108 doi: 10.1007/s10994-009-5152-4 – year: 2017 ident: ref33 article-title: Evolutionary multitasking for single-objective continuous optimization: Benchmark problems, performance metric, and baseline results publication-title: arXiv:1706.03470 – ident: ref61 doi: 10.1109/4235.996017 – ident: ref86 doi: 10.1109/SSCI.2016.7850040 – ident: ref67 doi: 10.1109/CEC.2009.4982949 – ident: ref26 doi: 10.1109/CEC48606.2020.9185660 – ident: ref16 doi: 10.1145/3205651.3205761 – ident: ref76 doi: 10.1109/TSMC.2018.2853719 – ident: ref116 doi: 10.1109/TEVC.2020.3004027 – ident: ref56 doi: 10.1109/TCYB.2020.2980888 – ident: ref94 doi: 10.1007/978-3-319-94472-2_10 – ident: ref40 doi: 10.1109/BigData47090.2019.9006274 – ident: ref122 doi: 10.1145/3341069.3342982 – ident: ref45 doi: 10.1109/CEC.2019.8789940 – ident: ref82 doi: 10.1007/978-981-15-3425-6_2 – ident: ref93 doi: 10.1109/TEVC.2021.3086308 – ident: ref117 doi: 10.1109/TCYB.2020.3017017 – ident: ref113 doi: 10.1109/CVPR.2017.316 – ident: ref52 doi: 10.1109/TCYB.2018.2845361 – ident: ref92 doi: 10.1007/s12293-021-00346-5 – ident: ref84 doi: 10.1007/s40747-016-0011-y – ident: ref74 doi: 10.1109/SSCI.2016.7850039 – ident: ref91 doi: 10.1109/TEVC.2021.3097043 – ident: ref103 doi: 10.1109/TKDE.2009.191 – ident: ref99 doi: 10.1109/TEVC.2018.2881955 – ident: ref41 doi: 10.1109/CEC.2019.8790352 – ident: ref121 doi: 10.1007/978-3-030-58115-2_44 – ident: ref34 doi: 10.1109/CEC.2016.7744178 – ident: ref115 doi: 10.1109/CVPR.2019.00283 – ident: ref57 doi: 10.1109/TEVC.2020.3023480 – ident: ref87 doi: 10.1109/SSCI.2016.7850038 – ident: ref126 doi: 10.1109/TEVC.2021.3110506 – ident: ref127 doi: 10.1109/CEC48606.2020.9185907 – ident: ref46 doi: 10.1109/TEVC.2017.2785351 – ident: ref9 doi: 10.1007/978-3-642-12683-3_34 – ident: ref109 doi: 10.1145/3386252 – ident: ref47 doi: 10.1109/CEC.2017.7969596 – ident: ref106 doi: 10.1109/TCYB.2020.3043509 – ident: ref1 doi: 10.1109/TEVC.2020.3044711 – ident: ref11 doi: 10.1007/s12559-016-9395-7 – ident: ref18 doi: 10.1109/TETC.2019.2945775 – ident: ref43 doi: 10.1109/CEC.2019.8790034 – ident: ref5 doi: 10.1109/TSC.2018.2827070 – ident: ref75 doi: 10.1109/TCYB.2019.2955599 – ident: ref62 doi: 10.1109/TEVC.2007.892759 – ident: ref80 doi: 10.1109/TCYB.2020.2974100 – ident: ref97 doi: 10.1145/3377929.3389934 – ident: ref129 doi: 10.1109/TEVC.2019.2955110 – ident: ref39 doi: 10.1109/TEVC.2014.2308294 – ident: ref54 doi: 10.1109/TCYB.2019.2962865 – ident: ref23 doi: 10.1016/j.engappai.2017.05.008 – ident: ref12 doi: 10.1109/MCI.2020.3039066 – ident: ref49 doi: 10.1109/TFUZZ.2020.2968863 – ident: ref3 doi: 10.1109/TCYB.2020.3013271 – ident: ref29 doi: 10.1109/CEC.2019.8789909 – ident: ref102 doi: 10.1109/CEC.2018.8477860 – ident: ref64 doi: 10.1109/CDC.2017.8263646
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Snippet	Evolutionary algorithms (EAs) possess strong problem-solving abilities and have been applied in a wide range of applications. However, they still suffer from a...
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SubjectTerms	Algorithms Costs Evolutionary algorithm (EA) Evolutionary algorithms Evolutionary computation evolutionary multitasking Multitasking Optimization Sociology Statistics Task analysis transfer optimization Trends
Title	A Review on Evolutionary Multitask Optimization: Trends and Challenges
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