乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T09:05:07Z","timestamp":1721120707509},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T00:00:00Z","timestamp":1641513600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T00:00:00Z","timestamp":1641513600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach. Intell. Res."],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s11633-022-1314-7","type":"journal-article","created":{"date-parts":[[2022,1,8]],"date-time":"2022-01-08T17:02:21Z","timestamp":1641661341000},"page":"138-152","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A Dynamic Resource Allocation Strategy with Reinforcement Learning for Multimodal Multi-objective Optimization"],"prefix":"10.1007","volume":"19","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-9295-1361","authenticated-orcid":false,"given":"Qian-Long","family":"Dang","sequence":"first","affiliation":[]},{"given":"Wei","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Yang-Fei","family":"Yuan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,7]]},"reference":[{"issue":"3","key":"1314_CR1","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1007\/s11633-021-1295-y","volume":"18","author":"S L Karri","year":"2021","unstructured":"S. L. Karri, L. C. De Silva, D. T. C. Lai, S. Y. Yong. Identification and classification of driving behaviour at signalized intersections using support vector machine. International Journal of Automation and Computing, vol. 18, no. 3, pp. 480\u2013491, 2021. DOI: https:\/\/doi.org\/10.1007\/s11633-021-1295-y.","journal-title":"International Journal of Automation and Computing"},{"issue":"6","key":"1314_CR2","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1007\/s11633-015-0917-7","volume":"17","author":"H T Ye","year":"2020","unstructured":"H. T. Ye, Z. Q. Li. PID neural network decoupling control based on hybrid particle swarm optimization and differential evolution. International Journal of Automation and Computing, vol. 17, no. 6, pp. 867\u2013872, 2020. DOI: https:\/\/doi.org\/10.1007\/s11633-015-0917-7.","journal-title":"International Journal of Automation and Computing"},{"issue":"3","key":"1314_CR3","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1007\/s11633-021-1292-1","volume":"18","author":"W Jia","year":"2021","unstructured":"W. Jia, W. Xia, Y. Zhao, H. Min, Y. X. Chen. 2D and 3D palmprint and palm vein recognition based on neural architecture search. International Journal of Automation and Computing, vol. 18, no. 3, pp. 377\u2013409, 2021. DOI: https:\/\/doi.org\/10.1007\/s11633-021-1292-1.","journal-title":"International Journal of Automation and Computing"},{"issue":"4","key":"1314_CR4","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1109\/TEVC.2002.802873","volume":"6","author":"A Jaszkiewicz","year":"2002","unstructured":"A. Jaszkiewicz. On the performance of multiple-objective genetic local search on the 0\/1 knapsack problem-a comparative experiment. IEEE Transactions on Evolutionary Computation, vol. 6, no. 4, pp. 402\u2013412, 2002. DOI: https:\/\/doi.org\/10.1109\/TEVC.2002.802873.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"3","key":"1314_CR5","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1162\/evco.1999.7.3.205","volume":"7","author":"K Deb","year":"1999","unstructured":"K. Deb. Multi-objective genetic algorithms: Problem difficulties and construction of test problems. Evolutionary Computation, vol. 7, no. 3, pp. 205\u2013230, 1999. DOI: https:\/\/doi.org\/10.1162\/evco.1999.7.3.205.","journal-title":"Evolutionary Computation"},{"issue":"6","key":"1314_CR6","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/TEVC.2007.892759","volume":"11","author":"Q F Zhang","year":"2007","unstructured":"Q. F. Zhang, H. Li. MOEA\/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, vol. 11, no. 6, pp. 712\u2013731, 2007. DOI: https:\/\/doi.org\/10.1109\/TEVC.2007.892759.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"2","key":"1314_CR7","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"K. Deb, A. Pratap, S. Agarwal, T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182\u2013197, 2002. DOI: https:\/\/doi.org\/10.1109\/4235.996017.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"6","key":"1314_CR8","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1007\/s11633-019-1184-9","volume":"16","author":"S Lu","year":"2019","unstructured":"S. Lu, Y. M. Li, B. X. Ding. Multi-objective dimensional optimization of a 3-DOF translational PKM considering transmission properties. International Journal of Automation and Computing, vol. 16, no. 6, pp. 748\u2013760, 2019. DOI: https:\/\/doi.org\/10.1007\/s11633-019-1184-9.","journal-title":"International Journal of Automation and Computing"},{"key":"1314_CR9","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.tcs.2018.07.007","volume":"773","author":"P S Oliveto","year":"2019","unstructured":"P. S. Oliveto, D. Sudholt, C. Zarges. On the benefits and risks of using fitness sharing for multimodal optimisation. Theoretical Computer Science, vol. 773, pp. 53\u201370, 2019. DOI: https:\/\/doi.org\/10.1016\/j.tcs.2018.07.007.","journal-title":"Theoretical Computer Science"},{"issue":"11\u201312","key":"1314_CR10","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1016\/S0965-9978(02)00045-5","volume":"33","author":"C Y Lin","year":"2002","unstructured":"C. Y. Lin, W. H. Wu. Niche identification techniques in multimodal genetic search with sharing scheme. Advances in Engineering Software, vol. 33, no. 11\u201312, pp. 779\u2013791, 2002. DOI: https:\/\/doi.org\/10.1016\/S0965-9978(02)00045-5.","journal-title":"Advances in Engineering Software"},{"key":"1314_CR11","doi-asserted-by":"publisher","first-page":"1382","DOI":"10.1109\/CEC.2004.1331058","volume-title":"Multimodal optimization using crowding-based differential evolution","author":"R Thomsen","year":"2004","unstructured":"R. Thomsen. Multimodal optimization using crowding-based differential evolution. In Proceedings of Congress on Evolutionary Computation, IEEE, Portland, USA, pp. 1382\u20131389, 2004. DOI: https:\/\/doi.org\/10.1109\/CEC.2004.1331058."},{"issue":"3","key":"1314_CR12","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1016\/j.asoc.2011.11.032","volume":"12","author":"M Q Li","year":"2012","unstructured":"M. Q. Li, D. Lin, J. S. Kou. A hybrid niching PSO enhanced with recombination-replacement crowding strategy for multimodal function optimization. Applied Soft Computing, vol. 12, no. 3, pp. 975\u2013987, 2012. DOI: https:\/\/doi.org\/10.1016\/j.asoc.2011.11.032.","journal-title":"Applied Soft Computing"},{"issue":"3","key":"1314_CR13","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1162\/106365602760234081","volume":"10","author":"J P Li","year":"2002","unstructured":"J. P. Li, M. E. Balazs, G. T. Parks, P. J. Clarkson. A species conserving genetic algorithm for multimodal function optimization. Evolutionary Computation, vol. 10, no. 3, pp. 207\u2013234, 2002. DOI: https:\/\/doi.org\/10.1162\/106365602760234081.","journal-title":"Evolutionary Computation"},{"issue":"8","key":"1314_CR14","doi-asserted-by":"publisher","first-page":"4836","DOI":"10.1109\/TSMC.2019.2944338","volume":"51","author":"Q Q Fan","year":"2021","unstructured":"Q. Q. Fan, X. F. Yan. Solving multimodal multiobjective problems through zoning search. IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 8, pp. 4836\u20134847, 2021. DOI: https:\/\/doi.org\/10.1109\/TSMC.2019.2944338.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"1314_CR15","volume-title":"Nonlinear Multiobjective Optimization","author":"K Miettinen","year":"1999","unstructured":"K. Miettinen. Nonlinear Multiobjective Optimization, Boston, USA: Kluwer Academic Publishers, 1999."},{"key":"1314_CR16","doi-asserted-by":"publisher","first-page":"2454","DOI":"10.1109\/CEC.2016.7744093","volume-title":"Multimodal multi-objective optimization: A preliminary study","author":"J J Liang","year":"2016","unstructured":"J. J. Liang, C. T. Yue, B. Y. Qu. Multimodal multi-objective optimization: A preliminary study. In Proceedings of IEEE Congress on Evolutionary Computation, IEEE, Vancouver, Canada, pp. 2454\u20132461, 2016. DOI: https:\/\/doi.org\/10.1109\/CEC.2016.7744093."},{"key":"1314_CR17","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1145\/3321707.3321759","volume-title":"Real-valued evolutionary multi-modal multi-objective optimization by hill-valley clustering","author":"S C Maree","year":"2019","unstructured":"S. C. Maree, T. Alderliesten, P. A. N. Bosman. Real-valued evolutionary multi-modal multi-objective optimization by hill-valley clustering. In Proceedings of the Genetic and Evolutionary Computation Conference, Association for Computing Machinery, Prague, Czech Republic, pp. 568\u2013576, 2019. DOI: https:\/\/doi.org\/10.1145\/3321707.3321759."},{"issue":"5","key":"1314_CR18","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1109\/TEVC.2017.2754271","volume":"22","author":"C T Yue","year":"2018","unstructured":"C. T. Yue, B. Y. Qu, J. Liang. A multi-objective particle swarm optimizer using ring topology for solving multimodal multi-objective problems. IEEE Transactions on Evolutionary Computation, vol. 22, no. 5, pp. 805\u2013817, 2018. DOI: https:\/\/doi.org\/10.1109\/TEVC.2017.2754271.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"1314_CR19","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/978-3-319-99253-2_20","volume-title":"A decomposition-based evolutionary algorithm for multi-modal multi-objective optimization","author":"R Tanabe","year":"2018","unstructured":"R. Tanabe, H. Ishibuchi. A decomposition-based evolutionary algorithm for multi-modal multi-objective optimization. In Proceedings of the 15th International Conference on Parallel Problem Solving from Nature, Springer, Coimbra, Portugal, pp. 249\u2013261, 2018. DOI: https:\/\/doi.org\/10.1007\/978-3-319-99253-2_20."},{"issue":"4","key":"1314_CR20","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1109\/TEVC.2019.2949841","volume":"24","author":"R Tanabe","year":"2020","unstructured":"R. Tanabe, H. Ishibuchi. A framework to handle multimodal multiobjective optimization in decomposition-based evolutionary algorithms. IEEE Transactions on Evolutionary Computation, vol. 24, no. 4, pp. 720\u2013734, 2020. DOI: https:\/\/doi.org\/10.1109\/TEVC.2019.2949841.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"1314_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/CEC48606.2020.9185674","volume-title":"A decomposition-based large-scale multi-modal multi-objective optimization algorithm","author":"Y M Peng","year":"2020","unstructured":"Y. M. Peng, H. Ishibuchi. A decomposition-based large-scale multi-modal multi-objective optimization algorithm. In Proceedings of IEEE Congress on Evolutionary Computation, IEEE, Glasgow, UK, pp. 1\u20138, 2020. DOI: https:\/\/doi.org\/10.1109\/CEC48606.2020.9185674."},{"key":"1314_CR22","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.swevo.2019.06.001","volume":"49","author":"R Tanabe","year":"2019","unstructured":"R. Tanabe, H. Ishibuchi. A niching indicator-based multimodal many-objective optimizer. Swarm and Evolutionary Computation, vol. 49, pp. 134\u2013146, 2019. DOI: https:\/\/doi.org\/10.1016\/j.swevo.2019.06.001.","journal-title":"Swarm and Evolutionary Computation"},{"key":"1314_CR23","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1109\/CEC.2009.4982949","volume-title":"The performance of a new version of MOEA\/D on CEC09 unconstrained MOP test instances","author":"Q F Zhang","year":"2009","unstructured":"Q. F. Zhang, W. D. Liu, H. Li. The performance of a new version of MOEA\/D on CEC09 unconstrained MOP test instances. In Proceedings of IEEE Congress on Evolutionary Computation, IEEE, Trondheim, Norway, pp. 203\u2013208, 2009. DOI: https:\/\/doi.org\/10.1109\/CEC.2009.4982949."},{"issue":"12","key":"1314_CR24","doi-asserted-by":"publisher","first-page":"2416","DOI":"10.1109\/TSMC.2018.2818175","volume":"49","author":"Q Kang","year":"2019","unstructured":"Q. Kang, X. Y. Song, M. C. Zhou, L. Li. A collaborative resource allocation strategy for decomposition-based multiobjective evolutionary algorithms. IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 12, pp. 2416\u20132423, 2019. DOI: https:\/\/doi.org\/10.1109\/TSMC.2018.2818175.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"issue":"1","key":"1314_CR25","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1109\/TEVC.2015.2424251","volume":"20","author":"A M Zhou","year":"2016","unstructured":"A. M. Zhou, Q. F. Zhang. Are all the subproblems equally important? Resource allocation in decomposition-baeed multiobjective evolutionary algorithms IEEE Transactions on Evolutionary Computation, vol. 20, no. 1, pp. 52\u201364, 2016. DOI: https:\/\/doi.org\/10.1109\/TEVC.2015.2424251.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"8","key":"1314_CR26","doi-asserted-by":"publisher","first-page":"2388","DOI":"10.1109\/TCYB.2017.2739185","volume":"48","author":"Q Z Lin","year":"2018","unstructured":"Q. Z. Lin, G. M. Jin, Y. P. Ma, K. C. Wong, C. A. Coello Coello, J. Q. Li, J. Y. Chen, J. Zhang. A diversrty-enhanced resource allocation strategy for decomposition-based multiobjective evolutionary algorithm. IEEE Transactions on Cybernetics, vol. 48, no. 8, pp. 2388\u20132401, 2018. DOI: https:\/\/doi.org\/10.1109\/TCYB.2017.2739185.","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"4","key":"1314_CR27","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1109\/TEVC.2014.2350995","volume":"19","author":"X Y Ca\u00ed","year":"2015","unstructured":"X. Y. Ca\u00ed, Y. X. Li, Z. Fan, Q. F. Zhang. An external archive guided multiobjective evolutionary algorithm based on decomposition for combinatorial optimization. IEEE Transactions on Evolutionary Computation, vol. 19, no. 4, pp. 508\u2013523, 2015. DOI: https:\/\/doi.org\/10.1109\/TEVC.2014.2350995.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"1","key":"1314_CR28","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1109\/TCYB.2017.2772250","volume":"49","author":"Y Xiang","year":"2019","unstructured":"Y. Xiang, Y. R. Zhou, L. P. Tang, Z. F. Chen. A decomposition-based many-objective artificial bee colony algorithm. IEEE Transactions on Cybernetics, vol. 49, no. 1, pp. 287\u2013300, 2019. DOI: https:\/\/doi.org\/10.1109\/TCYB.2017.2772250.","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"1","key":"1314_CR29","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1109\/TEVC.2013.2239648","volume":"18","author":"K Li","year":"2014","unstructured":"K. Li, A. Fialho, S. Kwong, Q. F. Zhang. Adaptive operator selection with bandits for a multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, vol. 18, no. 1, pp. 114\u2013130, 2014. DOI: https:\/\/doi.org\/10.1109\/TEVC.2013.2239648.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"3","key":"1314_CR30","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1109\/TEVC.2017.2725902","volume":"22","author":"H L Liu","year":"2018","unstructured":"H. L. Liu, L. Chen, Q. F. Zhang, K. Deb. Adaptively allocating search effort in challenging many-objective optimization problems. IEEE Transactions on Evolutionary Computation, vol. 22, no. 3, pp. 433\u2013448, 2018. DOI: https:\/\/doi.org\/10.1109\/TEVC.2017.2725902.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"3","key":"1314_CR31","doi-asserted-by":"publisher","first-page":"1507","DOI":"10.1109\/TSMC.2019.2898456","volume":"51","author":"H K Chen","year":"2021","unstructured":"H. K. Chen, G. H. Wu, W. Pedrycz, P. N. Suganthan, L. N. Xing, X. M. Zhu. An adaptive resource allocation strategy for objective space partition-based multiobjective optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 3, pp. 1507\u20131522, 2021. DOI: https:\/\/doi.org\/10.1109\/TSMC.2019.2898456.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"issue":"4","key":"1314_CR32","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1109\/TEVC.2021.3060899","volume":"25","author":"J J Zhou","year":"2021","unstructured":"J. J. Zhou, L. Gao, X. Y. Li. Ensemble of dynamic resource allocation strategies for decomposition-based multi-objective optimization. IEEE Transactions on Evolutionary Computation, vol. 25, no. 4, pp. 710\u2013723, 2021. DOI: https:\/\/doi.org\/10.1109\/TEVC.2021.3060899.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"3","key":"1314_CR33","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1109\/TEVC.2004.826069","volume":"8","author":"F van den Bergh","year":"2004","unstructured":"F. van den Bergh, A. P. Engelbrecht. A cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 225\u2013239, 2004. DOI: https:\/\/doi.org\/10.1109\/TEVC.2004.826069.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"1314_CR34","doi-asserted-by":"publisher","first-page":"1080","DOI":"10.1007\/11539117_147","volume-title":"Cooperative co-evolutionary differential evolution for function optimization","author":"Y J Shi","year":"2005","unstructured":"Y. J. Shi, H. F. Teng, Z. Q. Li. Cooperative co-evolutionary differential evolution for function optimization. In Proceedings of the 1st International Conference on Advances in Natural Computation, Springer, Changsha, China, pp. 1080\u20131088, 2005. DOI: https:\/\/doi.org\/10.1007\/11539117_147."},{"issue":"3","key":"1314_CR35","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1109\/TEVC.2005.857610","volume":"10","author":"J J Liang","year":"2006","unstructured":"J. J. Liang, A. K. Qin, P. N. Suganthan, S. Baskar. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation, vol. 10, no. 3, pp. 281\u2013295, 2006. DOI: https:\/\/doi.org\/10.1109\/TEVC.2005.857610.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"15","key":"1314_CR36","doi-asserted-by":"publisher","first-page":"2985","DOI":"10.1016\/j.ins.2008.02.017","volume":"178","author":"Z Y Yang","year":"2008","unstructured":"Z. Y. Yang, K. Tang, X. Yao. Large scale evolutionary optimization using cooperative coevolution. Information Sciences, vol. 178, no. 15, pp. 2985\u20132999, 2008. DOI: https:\/\/doi.org\/10.1016\/j.ins.2008.02.017.","journal-title":"Information Sciences"},{"issue":"4","key":"1314_CR37","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1109\/TEVC.2016.2627581","volume":"21","author":"M Yang","year":"2017","unstructured":"M. Yang, M. N. Omidvar, C. H. Li, X. D. Li, Z. H. Cai, B. Kazimipour, X. Yao. Efficient resource allocation in cooperative co-evolution for large-scale global optimization. IEEE Transactions on Evolutionary Computation, vol. 21, no. 4, pp. 493\u2013505, 2017. DOI: https:\/\/doi.org\/10.1109\/TEVC.2016.2627581.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"2","key":"1314_CR38","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1109\/TEVC.2018.2817889","volume":"23","author":"Y H Jia","year":"2019","unstructured":"Y. H. Jia, W. N. Chen, T. L. Gu, H. X. Zhang, H. Q. Yuan, S. Kwong, J. Zhang. Distributed cooperative co-evolution with adaptive computing resource allocation for large scale optimization. IEEE Transactions on Evolutionary Computation, vol. 23, no. 2, pp. 188\u2013202, 2019. DOI: https:\/\/doi.org\/10.1109\/TEVC.2018.2817889.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"1314_CR39","doi-asserted-by":"publisher","unstructured":"X. N. Shen, Y. N. Guo, A. M. Li. Cooperative coevolution with an improved resource allocation for large-scale multi-objective software project scheduling. Applied Soft Computing, vol. 88, Article number 106059, 2020. DOI: https:\/\/doi.org\/10.1016\/j.asoc.2019.106059.","DOI":"10.1016\/j.asoc.2019.106059"},{"key":"1314_CR40","doi-asserted-by":"publisher","unstructured":"Y. H. Jia, Y. Mei, M. J. Zhang. Contribution-based cooperative co-evolution for nonseparable large-scale problems with overlapping subcomponents. IEEE Transactions on Cybernetics, to be published. DOI: https:\/\/doi.org\/10.1109\/TCYB.2020.3025577.","DOI":"10.1109\/TCYB.2020.3025577"},{"issue":"5","key":"1314_CR41","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1109\/TEVC.2019.2893614","volume":"23","author":"M G Gong","year":"2019","unstructured":"M. G. Gong, Z. D. Tang, H. Li, J. Zhang. Evolutionary multitasking with dynamic resource allocating strategy. IEEE Transactions on Evolutionary Computation, vol. 23, no. 5, pp. 858\u2013869, 2019. DOI: https:\/\/doi.org\/10.1109\/TEVC.2019.2893614.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"6","key":"1314_CR42","doi-asserted-by":"publisher","first-page":"3857","DOI":"10.1109\/TII.2020.3015748","volume":"17","author":"Y Wang","year":"2021","unstructured":"Y. Wang, H. C. Tan, Y. K. Wu, J. K. Peng. Hybrid electric vehicle energy management with computer vision and deep reinforcement learning. IEEE Transactions on Industrial Informatics, vol. 17, no. 6, pp. 3857\u20133868, 2021. DOI: https:\/\/doi.org\/10.1109\/TII.2020.3015748.","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"3","key":"1314_CR43","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1109\/TNNLS.2016.2522401","volume":"28","author":"Y Deng","year":"2017","unstructured":"Y. Deng, F. Bao, Y. Y. Kong, Z. Q. Ren, Q. H. Dai. Deep direct reinforcement learning for financial signal representation and trading. IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 3, pp. 653\u2013664, 2017. DOI: https:\/\/doi.org\/10.1109\/TNNLS.2016.2522401.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"2","key":"1314_CR44","doi-asserted-by":"publisher","first-page":"976","DOI":"10.1109\/TII.2018.2883991","volume":"15","author":"Y X Wang","year":"2019","unstructured":"Y. X. Wang, K. Wang, H. W. Huang, T. M. Miyazaki, S. Guo. Traffic and computation co-offloading with reinforcement learning in fog computing for industrial applications. IEEE Transactions on Industrial Informatics, vol. 15, no. 2, pp. 976\u2013986, 2019. DOI: https:\/\/doi.org\/10.1109\/TII.2018.2883991.","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"1314_CR45","doi-asserted-by":"publisher","first-page":"19","DOI":"10.18653\/v1\/P18-5007","volume-title":"Deep reinforcement learning for NLP","author":"W Y Wang","year":"2018","unstructured":"W. Y. Wang, J. W. Li, X. D. He. Deep reinforcement learning for NLP. in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts, Association for Computational Linguistics, Melbourne, Australia, pp. 19\u201321, 2018. DOI: https:\/\/doi.org\/10.18653\/v1\/P18-5007."},{"issue":"4","key":"1314_CR46","doi-asserted-by":"publisher","first-page":"1669","DOI":"10.1109\/TCYB.2018.2878977","volume":"50","author":"Z Q Wan","year":"2020","unstructured":"Z. Q. Wan, C. Jiang, M. Fahad, Z. Ni, Y. Guo, H. B. He. Robot-assisted pedestrian regulation based on deep reinforcement learning. IEEE Transactions on Cybernetics, vol. 50, no. 4, pp. 1669\u20131682, 2020. DOI: https:\/\/doi.org\/10.1109\/TCYB.2018.2878977.","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"4","key":"1314_CR47","doi-asserted-by":"publisher","first-page":"3698","DOI":"10.1109\/TSG.2018.2834219","volume":"10","author":"E Mocanu","year":"2019","unstructured":"E. Mocanu, D. C. Mocanu, P. H. Nguyen, A. Liotta, M. E. Webber, M. Gibescu, J. G. Slootweg. On-line building energy optimization using deep reinforcement learning. IEEE Transactions on Smart Grid, vol. 10, no. 4, pp. 3698\u20133708, 2019. DOI: https:\/\/doi.org\/10.1109\/TSG.2018.2834219.","journal-title":"IEEE Transactions on Smart Grid"},{"issue":"6","key":"1314_CR48","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1109\/TEVC.2014.2378512","volume":"19","author":"X Y Zhang","year":"2015","unstructured":"X. Y. Zhang, Y. Tian, Y. C. Jin. A knee point-driven evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation, vol. 19, no. 6, pp. 761\u2013776, 2015. DOI: https:\/\/doi.org\/10.1109\/TEVC.2014.2378512.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"1314_CR49","first-page":"10158","volume-title":"Tuning-free plug-and-play proximal algorithm for inverse imaging problems","author":"K X Wei","year":"2020","unstructured":"K. X. Wei, A. Aviles-Rivero, J. W. Liang, Y. Fu, C. B. Schonlieb, H. Huang. Tuning-free plug-and-play proximal algorithm for inverse imaging problems. In Proceedings of the 37th International Conference on Machine Learning, ICML, Vienna, Austria, pp. 10158\u201310169, 2020."},{"key":"1314_CR50","doi-asserted-by":"publisher","DOI":"10.1314\/2.2.33423.64164","volume-title":"Problem definitions and evaluation criteria for the CEC 2019 special session on multimodal multiobjective optimization","author":"J Liang","year":"2019","unstructured":"J. Liang, B. Y. Qu, D. W. Gong, C. T. Yue. Problem definitions and evaluation criteria for the CEC 2019 special session on multimodal multiobjective optimization. Technical Report, Zhengzhou University, China, 2019. DOI: https:\/\/doi.org\/10.1314\/2.2.33423.64164."},{"issue":"4","key":"1314_CR51","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/4235.797969","volume":"3","author":"E Zitzler","year":"1999","unstructured":"E. Zitzler, L. Thiele. Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, vol. 3, no. 4, pp. 257\u2013271, 1999. DOI: https:\/\/doi.org\/10.1109\/4235.797969.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"5","key":"1314_CR52","doi-asserted-by":"publisher","first-page":"2088","DOI":"10.1109\/TSMCB.2004.834438","volume":"34","author":"S Bandyopadhyay","year":"2004","unstructured":"S. Bandyopadhyay, S. K. Pal, B. Aruna. Multiobjective GAs, quantitative indices, and pattern classification. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, vol. 34, no. 5, pp. 2088\u20132099, 2004. DOI: https:\/\/doi.org\/10.1109\/TSMCB.2004.834438.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics"},{"key":"1314_CR53","doi-asserted-by":"publisher","unstructured":"B. Y. Qu, C. Li, J. Liang, L. Yan, K. J. Yu, Y. S. Zhu. A self-organized speciation based multi-objective particle swarm optimizer for multimodal multi-objective problems. Applied Soft Computing, vol. 86, Article number 105886, 2020. DOI: https:\/\/doi.org\/10.1016\/j.asoc.2019.105886.","DOI":"10.1016\/j.asoc.2019.105886"},{"issue":"200","key":"1314_CR54","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","volume":"32","author":"M Friedman","year":"1937","unstructured":"M. Friedman. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, vol. 32, no. 200, pp. 675\u2013701, 1937. DOI: https:\/\/doi.org\/10.1080\/01621459.1937.10503522.","journal-title":"Journal of the American Statistical Association"},{"issue":"3","key":"1314_CR55","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1109\/TEVC.2019.2938557","volume":"24","author":"Y Liu","year":"2020","unstructured":"Y. Liu, H. Ishibuchi, G. G. Yen, Y. Nojima, N. Masuyama. Handling imbalance between convergence and diversity in the decision space in evolutionary multi-modal multi-objective optimization. IEEE Transactions on Evolutionary Computation, vol. 24, no. 3, pp. 551\u2013565, 2020. DOI: https:\/\/doi.org\/10.1109\/TEVC.2019.2938557.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"1314_CR56","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1007\/978-3-319-99253-2_21","volume-title":"A double-niched evolutionary algorithm and its behavior on polygon-based problems","author":"Y P Liu","year":"2018","unstructured":"Y. P. Liu, H. Ishibuchi, Y. Nojima, N. Masuyama, K. Shang. A double-niched evolutionary algorithm and its behavior on polygon-based problems. In Proceedings of the 15th International Conference on Parallel Problem Solving from Nature, Springer, Coimbra, Portugal, pp. 262\u2013273, 2018. DOI: https:\/\/doi.org\/10.1007\/978-3-319-99253-2_21."},{"issue":"4","key":"1314_CR57","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1109\/TEVC.2018.2879406","volume":"23","author":"Y P Liu","year":"2019","unstructured":"Y. P. Liu, G. G. Yen, D. W. Gong. A multimodal multiobjective evolutionary algorithm using two-archive and recombination strategies. IEEE Transactions on Evolutionary Computation, vol. 23, no. 4, pp. 660\u2013674, 2019. DOI: https:\/\/doi.org\/10.1109\/TEVC.2018.2879406.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"3","key":"1314_CR58","doi-asserted-by":"publisher","first-page":"1062","DOI":"10.1016\/j.ejor.2006.06.042","volume":"185","author":"K Deb","year":"2008","unstructured":"K. Deb, S. Tiwari. Omni-optimizer: A generic evolutionary algorithm for single and multi-objective optimization. European Journal of Operational Research, vol. 185, no. 3, pp. 1062\u20131087, 2008. DOI: https:\/\/doi.org\/10.1016\/j.ejor.2006.06.042.","journal-title":"European Journal of Operational Research"},{"issue":"5","key":"1314_CR59","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1109\/TEVC.2009.2021467","volume":"13","author":"A M Zhou","year":"2009","unstructured":"A. M. Zhou, Q. F. Zhang, Y. C. Jin. Approximating the set of Pareto-optimal solutions in both the decision and objective spaces by an estimation of distribution algorithm. IEEE Transactions on Evolutionary Computation, vol. 13, no. 5, pp. 1167\u20131189, 2009. DOI: https:\/\/doi.org\/10.1109\/TEVC.2009.2021467.","journal-title":"IEEE Transactions on Evolutionary Computation"}],"container-title":["Machine Intelligence Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11633-022-1314-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11633-022-1314-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11633-022-1314-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,25]],"date-time":"2022-10-25T10:42:38Z","timestamp":1666694558000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11633-022-1314-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,7]]},"references-count":59,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["1314"],"URL":"https:\/\/doi.org\/10.1007\/s11633-022-1314-7","relation":{},"ISSN":["2731-538X","2731-5398"],"issn-type":[{"value":"2731-538X","type":"print"},{"value":"2731-5398","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,7]]},"assertion":[{"value":"15 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}