Conditional Generative Adversarial Network-Based Bilevel Evolutionary Multiobjective Optimization Algorithm
In bilevel multiobjective optimization problems (BLMOPs), the mapping from an upper-level vector to the corresponding lower-level optimal vectors is a complex set valued mapping. Existing methods require numerous surrogate models to fit such a set valued mapping by grouping the lower-level optimal v...
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| Vydáno v: | IEEE transactions on evolutionary computation Ročník 28; číslo 5; s. 1205 - 1219 |
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| Jazyk: | angličtina |
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01.10.2024
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| ISSN: | 1089-778X, 1941-0026 |
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| Abstract | In bilevel multiobjective optimization problems (BLMOPs), the mapping from an upper-level vector to the corresponding lower-level optimal vectors is a complex set valued mapping. Existing methods require numerous surrogate models to fit such a set valued mapping by grouping the lower-level optimal vectors, and the effects are not satisfactory because the correlation among lower-level optimal vectors corresponding to the same upper-level vector is disregarded. In this article, introducing conditional generative adversarial network (cGAN), we use only one surrogate model to effectively fit such a set valued mapping, which extracts knowledge from lower-level optimal vectors corresponding to the same upper-level vector. Then, a BLMOP is transformed into a single-level constraint multiobjective optimization problem (CMOP). By adaptively allocating computational resources to optimize the CMOP, promising upper-level vectors are obtained. Furthermore, a lower-level search is executed for these promising upper-level vectors, thus obtaining high-quality solutions. Because of the excellent performance of cGAN and the lower-level search conducted only for promising upper-level vectors, the computational overhead is greatly reduced. The proposed algorithm has achieved the best results in comparison with five state-of-the-art algorithms on benchmark problems and a real-world problem, whose effectiveness has been demonstrated. |
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| AbstractList | In bilevel multiobjective optimization problems (BLMOPs), the mapping from an upper-level vector to the corresponding lower-level optimal vectors is a complex set valued mapping. Existing methods require numerous surrogate models to fit such a set valued mapping by grouping the lower-level optimal vectors, and the effects are not satisfactory because the correlation among lower-level optimal vectors corresponding to the same upper-level vector is disregarded. In this article, introducing conditional generative adversarial network (cGAN), we use only one surrogate model to effectively fit such a set valued mapping, which extracts knowledge from lower-level optimal vectors corresponding to the same upper-level vector. Then, a BLMOP is transformed into a single-level constraint multiobjective optimization problem (CMOP). By adaptively allocating computational resources to optimize the CMOP, promising upper-level vectors are obtained. Furthermore, a lower-level search is executed for these promising upper-level vectors, thus obtaining high-quality solutions. Because of the excellent performance of cGAN and the lower-level search conducted only for promising upper-level vectors, the computational overhead is greatly reduced. The proposed algorithm has achieved the best results in comparison with five state-of-the-art algorithms on benchmark problems and a real-world problem, whose effectiveness has been demonstrated. |
| Author | Wang, Weizhong Liu, Hai-Lin |
| Author_xml | – sequence: 1 givenname: Weizhong orcidid: 0009-0000-9891-6516 surname: Wang fullname: Wang, Weizhong email: weizhong-wang@foxmail.com organization: School of Automation, Guangdong University of Technology, Guangzhou, China – sequence: 2 givenname: Hai-Lin orcidid: 0000-0003-2276-1938 surname: Liu fullname: Liu, Hai-Lin email: hlliu@gdut.edu.cn organization: School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou, China |
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| References_xml | – year: 2019 ident: ref25 article-title: Benchmarking regression methods: A comparison with CGAN publication-title: arXiv:1905.12868 – volume: 66 start-page: 15 year: 2017 ident: ref8 article-title: Spatial targeting of agri-environmental policy using bilevel evolutionary optimization publication-title: Omega doi: 10.1016/j.omega.2016.01.007 – ident: ref1 doi: 10.1287/opre.1090.0695 – ident: ref13 doi: 10.4236/ajor.2013.32026 – ident: ref23 doi: 10.1109/TCYB.2021.3061551 – ident: ref12 doi: 10.1109/CEC.2012.6256156 – ident: ref2 doi: 10.1287/inte.2013.0700 – ident: ref22 doi: 10.1109/TCYB.2022.3175533 – ident: ref19 doi: 10.1155/2012/102482 – ident: ref28 doi: 10.1109/4235.797969 – ident: ref31 doi: 10.1109/4235.996017 – ident: ref3 doi: 10.1002/atr.1211 – ident: ref11 doi: 10.1109/TEVC.2022.3154057 – ident: ref5 doi: 10.1145/2739480.2754732 – volume: 2012 start-page: 359 year: 2012 ident: ref18 article-title: An improved particle swarm optimization for solving bilevel multiobjective programming problem publication-title: J. Appl. Math. – ident: ref30 doi: 10.1145/3520304.3529045 – volume: 53 start-page: 13 year: 2013 ident: ref20 article-title: Solving high dimensional bilevel multiobjective programming problem using a hybrid particle swarm optimization algorithm with crossover operator publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2013.07.015 – volume: 11 start-page: 1 year: 2015 ident: ref7 article-title: Valuing water quality tradeoffs at different spatial scales: An integrated approach using bilevel optimization publication-title: Water Resourc. Econ. doi: 10.1016/j.wre.2015.06.002 – ident: ref21 doi: 10.1080/00207543.2021.1999523 – ident: ref14 doi: 10.1515/cait-2017-0030 – ident: ref4 doi: 10.1371/journal.pone.0128067 – year: 2018 ident: ref24 article-title: cGANs with projection discriminator publication-title: arXiv:1802.05637 – ident: ref9 doi: 10.1007/s11590-015-0918-z – ident: ref27 doi: 10.1080/09540091.2022.2077312 – ident: ref26 doi: 10.1109/TCYB.2020.2985081 – ident: ref33 doi: 10.1109/TEVC.2003.810761 – volume: 77 start-page: 194 year: 2017 ident: ref17 article-title: Approximated set-valued mapping approach for handling multiobjective bilevel problems publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2016.08.001 – ident: ref15 doi: 10.1162/evco_a_00015 – ident: ref10 doi: 10.1155/2016/9109824 – year: 2014 ident: ref32 article-title: Adam: A method for stochastic optimization publication-title: arXiv:1412.6980 – volume: 80 start-page: 1568 volume-title: Proc. 35th Int. Conf. Mach. Learn. ident: ref6 article-title: Bilevel programming for hyperparameter optimization and meta-learning – ident: ref16 doi: 10.1109/ACCESS.2020.3013568 – volume: 20 start-page: 495 issue: 5 year: 2007 ident: ref29 article-title: Decentralized multi-objective bilevel decision making with fuzzy demands publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2007.01.003 |
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| SubjectTerms | Adaptation models Bilevel optimization Computational modeling conditional generative adversarial network (cGAN) Correlation evolutionary algorithm Generators multiobjective Optimization Sociology Task analysis |
| Title | Conditional Generative Adversarial Network-Based Bilevel Evolutionary Multiobjective Optimization Algorithm |
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