A pairwise comparison based surrogate-assisted evolutionary algorithm for expensive multi-objective optimization
Multi-objective optimization problems in many real-world applications are characterized by computationally or economically expensive objectives, which cannot provide sufficient function evaluations for evolutionary algorithms to converge. Thus, a variety of surrogate models have been employed to pro...
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| Vydané v: | Swarm and evolutionary computation Ročník 80; s. 101323 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.07.2023
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| ISSN: | 2210-6502 |
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| Abstract | Multi-objective optimization problems in many real-world applications are characterized by computationally or economically expensive objectives, which cannot provide sufficient function evaluations for evolutionary algorithms to converge. Thus, a variety of surrogate models have been employed to provide much more virtual evaluations. Most existing surrogate models are essentially regressors or classifiers, which may suffer from low reliability in the approximation of complex objectives. In this paper, we propose a novel surrogate-assisted evolutionary algorithm, which employs a surrogate model to conduct pairwise comparisons between candidate solutions, rather than directly predicting solutions’ fitness values. In comparison to regression and classification models, the proposed pairwise comparison based model can better balance between positive and negative samples, and may be directly used, reversely used, or ignored according to its reliability in model management. As demonstrated by the experimental results on abundant benchmark and real-world problems, the proposed surrogate model is more accurate than popular surrogate models, leading to performance superiority over state-of-the-art surrogate models. |
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| AbstractList | Multi-objective optimization problems in many real-world applications are characterized by computationally or economically expensive objectives, which cannot provide sufficient function evaluations for evolutionary algorithms to converge. Thus, a variety of surrogate models have been employed to provide much more virtual evaluations. Most existing surrogate models are essentially regressors or classifiers, which may suffer from low reliability in the approximation of complex objectives. In this paper, we propose a novel surrogate-assisted evolutionary algorithm, which employs a surrogate model to conduct pairwise comparisons between candidate solutions, rather than directly predicting solutions’ fitness values. In comparison to regression and classification models, the proposed pairwise comparison based model can better balance between positive and negative samples, and may be directly used, reversely used, or ignored according to its reliability in model management. As demonstrated by the experimental results on abundant benchmark and real-world problems, the proposed surrogate model is more accurate than popular surrogate models, leading to performance superiority over state-of-the-art surrogate models. |
| ArticleNumber | 101323 |
| Author | Zhang, Xingyi Ma, Haiping Tian, Ye He, Cheng Zhang, Limiao Hu, Jiaxing |
| Author_xml | – sequence: 1 givenname: Ye orcidid: 0000-0002-3487-5126 surname: Tian fullname: Tian, Ye organization: Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, China – sequence: 2 givenname: Jiaxing surname: Hu fullname: Hu, Jiaxing organization: School of Computer Science and Technology, Anhui University, Hefei, China – sequence: 3 givenname: Cheng surname: He fullname: He, Cheng organization: School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, China – sequence: 4 givenname: Haiping surname: Ma fullname: Ma, Haiping organization: Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, China – sequence: 5 givenname: Limiao orcidid: 0000-0002-0072-4047 surname: Zhang fullname: Zhang, Limiao organization: Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, China – sequence: 6 givenname: Xingyi orcidid: 0000-0002-5052-000X surname: Zhang fullname: Zhang, Xingyi email: xyzhanghust@gmail.com organization: Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, China |
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| Cites_doi | 10.1093/nar/gkq322 10.1016/j.swevo.2023.101230 10.1109/TEVC.2017.2749619 10.1007/978-3-319-09584-4_29 10.1109/JAS.2022.105875 10.1109/CEC.2015.7257247 10.1007/s40747-017-0039-7 10.1109/TEVC.2016.2622301 10.1109/TEVC.2013.2262178 10.1016/j.swevo.2022.101141 10.1023/A:1022627411411 10.2514/6.1996-4161 10.1109/TEVC.2003.810761 10.1016/j.swevo.2011.03.001 10.1109/CEC.2005.1555050 10.1007/s00500-017-2965-0 10.1109/TCYB.2019.2906383 10.1016/j.swevo.2022.101176 10.1109/TEVC.2005.861417 10.1109/TEVC.2018.2869001 10.1109/CEC.2018.8477730 10.1109/CEC.2005.1554717 10.1109/TCYB.2018.2794503 10.1109/TEVC.2022.3152582 10.1016/j.neucom.2022.10.075 10.1109/4235.996017 10.1016/j.swevo.2020.100774 10.1109/MCI.2017.2742868 10.1109/MCI.2009.933094 10.1080/00401706.2000.10485979 10.1109/4235.797969 10.1016/j.swevo.2022.101081 10.1109/TEVC.2018.2802784 10.1080/03052150108940926 10.1109/TEVC.2017.2693320 10.1016/j.swevo.2023.101252 10.2118/201229-PA 10.1109/MHS.1995.494215 10.1109/TEVC.2007.892759 10.1016/j.swevo.2022.101146 10.1109/TETCI.2018.2872055 10.1109/TEVC.2013.2281534 10.1016/j.neucom.2023.03.073 10.1109/TEVC.2014.2308305 10.1016/j.cor.2019.104869 10.1287/ijoc.2017.0749 10.1109/TEVC.2020.3044711 10.1016/j.swevo.2022.101170 10.1109/TEVC.2013.2248012 10.1145/1102351.1102363 |
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| Keywords | Evolutionary algorithms Surrogate-assisted optimization Pairwise comparison Expensive multi-objective optimization |
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| References | T.W. Simpson, W. Chen, J.K. Allen, F. Mistree, Conceptual design of a family of products through the use of the robust concept exploration method, in: Proceedings of the AIAA/NASA/USAF/ISSMO Symposium on Multidisciplinary Analysis and Optimization, 1996. Chugh, Jin, Miettinen, Hakanen, Sindhya (b21) 2016; 22 Tian, Chen, Ma, Zhang, Tan, Yaochu (b10) 2022 Tang, Wang, Xiong (b16) 2023; 77 Jin, Wang, Chugh, Guo, Miettinen (b15) 2019; 23 S. Kukkonen, J. Lampinen, GDE3: The third evolution step of generalized differential evolution, in: Proceedings of the 2005 IEEE Congress on Evolutionary Computation, 2005, pp. 443–450. Tian, Peng, Yang, Zhang, Tan, Jin (b34) 2021 Zhang, Li (b8) 2007; 11 Mamun, Singh, Ray (b25) 2022; 75 Zhao, Zhang, Chen, Zhao, Yao, Yao, Zhang, Yang (b33) 2020; 25 Pan, He, Tian, Wang, Zhang, Jin (b24) 2018; 23 Deb, Agrawal (b42) 1995; 9 Ray, Tai, Seow (b52) 2001; 33 C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, G. Hullender, Learning to rank using gradient descent, in: Proceedings of the 22nd International Conference on Machine Learning, 2005, pp. 89–96. Guo, Wang, Gao, Jin, Ding, Chai (b22) 2021 Jin, Sendhoff (b12) 2009; 4 Tian, Zhu, Zhang, Jin (b45) 2023; 518 Zhou, Qu, Li, Zhao, Suganthan, Zhang (b3) 2011; 1 E. Zitzler, M. Laumanns, L. Thiele, SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization, in: Proceedings of the Fifth Conference on Evolutionary Methods for Design, Optimization and Control with Applications To Industrial Problems, 2001, pp. 95–100. Naharro, Toharia, LaTorre, Peña (b28) 2022; 75 Yuan, Banzhaf (b35) 2021 Wang, Lou, Dong, Yu, Lu (b2) 2023; 77 Li, Gao, Garg, Shen, Huang (b30) 2021; 60 Tian, Zheng, Zhang, Jin (b41) 2020; 50 Tian, Cheng, Zhang, Jin (b44) 2017; 12 Liu, Zhang, Gielen (b27) 2013; 18 Z. Zhou, Y.S. Ong, M.H. Nguyen, D. Lim, A study on polynomial regression and Gaussian process global surrogate model in hierarchical surrogate-assisted evolutionary algorithm, in: Proceedings of the 2005 IEEE Congress on Evolutionary Computation, 3, 2005, pp. 2832–2839. Cortes, Vapnik (b18) 1995; 20 J. Zhang, A. Zhou, G. Zhang, A classification and Pareto domination based multiobjective evolutionary algorithm, in: Proceedings of the 2015 IEEE Congress on Evolutionary Computation, 2015, pp. 2883–2890. Deb, Thiele, Laumanns, Zitzler (b47) 2005 Douguet (b11) 2010; 38 Namura, Shimoyama, Obayashi (b26) 2017; 21 Guo, Jin, Ding, Chai (b31) 2018; 49 Zhang, Tian, Cheng, Jin (b7) 2015; 19 R. Eberhart, J. Kennedy, A new optimizer using particle swarm theory, in: Proceedings of the 6th International Symposium on Micro Machine and Human Science, 1995, pp. 39–43. Tian, Cheng, Zhang, Cheng, Jin (b9) 2018; 22 Tian, Yang, Zhang, Duan, Zhang (b39) 2019; 3 Huband, Hingston, Barone, While (b48) 2006; 10 I. Steponavičė, R.J. Hyndman, K. Smith-Miles, L. Villanova, Efficient identification of the Pareto optimal set, in: Proceedings of the 2014 International Conference on Learning and Intelligent Optimization, 2014, pp. 341–352. Cheng, Li, Tian, Zhang, Yang, Jin, Yao (b49) 2017; 3 Zitzler, Thiele (b55) 1999; 3 Tian, Liu, Zhang, Ma, Tan, Jin (b59) 2021; 25 McKay, Beckman, Conover (b36) 2000; 42 Li, Yang, Liu (b40) 2013; 18 Müller (b46) 2017; 29 Powers (b56) 2020 Deb, Pratap, Agarwal, Meyarivan (b4) 2002; 6 Wu, Wang, Xu, Hu, Xu (b19) 2022; 75 Y. Tian, X. Xiang, X. Zhang, R. Cheng, Y. Jin, Sampling reference points on the Pareto fronts of benchmark multi-objective optimization problems, in: Proceedings of the 2018 IEEE Congress on Evolutionary Computation, 2018. Specht (b38) 1996 Chugh, Sindhya, Hakanen, Miettinen (b13) 2019; 23 Zhang, Li, Zhao, Qi, Liu (b20) 2022; 72 Bosman, Thierens (b53) 2003; 7 Jain, Deb (b50) 2014; 18 He, He, Duan, Zhong (b1) 2022; 75 Deb, Goyal (b43) 1996; 26 Hao, Zhou, Qian, Zhang (b29) 2022 Zhang, Yu, Jin, Qian (b58) 2023; 538 Rojas-Gonzalez, Nieuwenhuyse (b14) 2020; 116 Tian (10.1016/j.swevo.2023.101323_b34) 2021 Zhang (10.1016/j.swevo.2023.101323_b20) 2022; 72 Zhao (10.1016/j.swevo.2023.101323_b33) 2020; 25 Tian (10.1016/j.swevo.2023.101323_b59) 2021; 25 Cheng (10.1016/j.swevo.2023.101323_b49) 2017; 3 Tian (10.1016/j.swevo.2023.101323_b39) 2019; 3 Mamun (10.1016/j.swevo.2023.101323_b25) 2022; 75 Zitzler (10.1016/j.swevo.2023.101323_b55) 1999; 3 Jin (10.1016/j.swevo.2023.101323_b12) 2009; 4 10.1016/j.swevo.2023.101323_b57 Zhang (10.1016/j.swevo.2023.101323_b7) 2015; 19 Deb (10.1016/j.swevo.2023.101323_b42) 1995; 9 10.1016/j.swevo.2023.101323_b17 Tian (10.1016/j.swevo.2023.101323_b9) 2018; 22 Jin (10.1016/j.swevo.2023.101323_b15) 2019; 23 Müller (10.1016/j.swevo.2023.101323_b46) 2017; 29 Ray (10.1016/j.swevo.2023.101323_b52) 2001; 33 McKay (10.1016/j.swevo.2023.101323_b36) 2000; 42 Namura (10.1016/j.swevo.2023.101323_b26) 2017; 21 Zhang (10.1016/j.swevo.2023.101323_b58) 2023; 538 Deb (10.1016/j.swevo.2023.101323_b47) 2005 10.1016/j.swevo.2023.101323_b32 Specht (10.1016/j.swevo.2023.101323_b38) 1996 Tian (10.1016/j.swevo.2023.101323_b41) 2020; 50 Tian (10.1016/j.swevo.2023.101323_b44) 2017; 12 10.1016/j.swevo.2023.101323_b23 Zhou (10.1016/j.swevo.2023.101323_b3) 2011; 1 Deb (10.1016/j.swevo.2023.101323_b43) 1996; 26 Hao (10.1016/j.swevo.2023.101323_b29) 2022 Huband (10.1016/j.swevo.2023.101323_b48) 2006; 10 10.1016/j.swevo.2023.101323_b6 Chugh (10.1016/j.swevo.2023.101323_b13) 2019; 23 Li (10.1016/j.swevo.2023.101323_b30) 2021; 60 10.1016/j.swevo.2023.101323_b5 Guo (10.1016/j.swevo.2023.101323_b22) 2021 Douguet (10.1016/j.swevo.2023.101323_b11) 2010; 38 Guo (10.1016/j.swevo.2023.101323_b31) 2018; 49 He (10.1016/j.swevo.2023.101323_b1) 2022; 75 Bosman (10.1016/j.swevo.2023.101323_b53) 2003; 7 Chugh (10.1016/j.swevo.2023.101323_b21) 2016; 22 Pan (10.1016/j.swevo.2023.101323_b24) 2018; 23 Wu (10.1016/j.swevo.2023.101323_b19) 2022; 75 10.1016/j.swevo.2023.101323_b37 Naharro (10.1016/j.swevo.2023.101323_b28) 2022; 75 Cortes (10.1016/j.swevo.2023.101323_b18) 1995; 20 Tang (10.1016/j.swevo.2023.101323_b16) 2023; 77 Zhang (10.1016/j.swevo.2023.101323_b8) 2007; 11 Jain (10.1016/j.swevo.2023.101323_b50) 2014; 18 Tian (10.1016/j.swevo.2023.101323_b10) 2022 Deb (10.1016/j.swevo.2023.101323_b4) 2002; 6 10.1016/j.swevo.2023.101323_b54 10.1016/j.swevo.2023.101323_b51 Rojas-Gonzalez (10.1016/j.swevo.2023.101323_b14) 2020; 116 Yuan (10.1016/j.swevo.2023.101323_b35) 2021 Powers (10.1016/j.swevo.2023.101323_b56) 2020 Li (10.1016/j.swevo.2023.101323_b40) 2013; 18 Wang (10.1016/j.swevo.2023.101323_b2) 2023; 77 Tian (10.1016/j.swevo.2023.101323_b45) 2023; 518 Liu (10.1016/j.swevo.2023.101323_b27) 2013; 18 |
| References_xml | – volume: 10 start-page: 477 year: 2006 end-page: 506 ident: b48 article-title: A review of multiobjective test problems and a scalable test problem toolkit publication-title: IEEE Trans. Evol. Comput. – volume: 18 start-page: 602 year: 2014 end-page: 622 ident: b50 article-title: An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part II: Handling constraints and extending to an adaptive approach publication-title: IEEE Trans. Evol. Comput. – reference: Z. Zhou, Y.S. Ong, M.H. Nguyen, D. Lim, A study on polynomial regression and Gaussian process global surrogate model in hierarchical surrogate-assisted evolutionary algorithm, in: Proceedings of the 2005 IEEE Congress on Evolutionary Computation, 3, 2005, pp. 2832–2839. – year: 2020 ident: b56 article-title: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation – volume: 4 start-page: 62 year: 2009 end-page: 76 ident: b12 article-title: A systems approach to evolutionary multiobjective structural optimization and beyond publication-title: IEEE Comput. Intell. Mag. – volume: 42 start-page: 55 year: 2000 end-page: 61 ident: b36 article-title: A comparison of three methods for selecting values of input variables in the analysis of output from a computer code publication-title: Technometrics – start-page: 105 year: 2005 end-page: 145 ident: b47 article-title: Scalable test problems for evolutionary multiobjective optimization publication-title: Evolutionary Multiobjective Optimization – volume: 72 year: 2022 ident: b20 article-title: A convolutional neural network-based surrogate model for multi-objective optimization evolutionary algorithm based on decomposition publication-title: Swarm Evol. Comput. – reference: T.W. Simpson, W. Chen, J.K. Allen, F. Mistree, Conceptual design of a family of products through the use of the robust concept exploration method, in: Proceedings of the AIAA/NASA/USAF/ISSMO Symposium on Multidisciplinary Analysis and Optimization, 1996. – volume: 38 start-page: W615 year: 2010 end-page: W621 ident: b11 article-title: e-LEA3D: A computational-aided drug design web server publication-title: Nucleic Acids Res. – volume: 23 start-page: 3137 year: 2019 end-page: 3166 ident: b13 article-title: A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms publication-title: Soft Comput. – volume: 49 start-page: 1012 year: 2018 end-page: 1025 ident: b31 article-title: Heterogeneous ensemble-based infill criterion for evolutionary multiobjective optimization of expensive problems publication-title: IEEE Trans. Cybern. – volume: 33 start-page: 399 year: 2001 end-page: 424 ident: b52 article-title: An evolutionary algorithm for multiobjective optimization publication-title: Eng. Optim. – volume: 75 year: 2022 ident: b19 article-title: Adaptive surrogate-assisted multi-objective evolutionary algorithm using an efficient infill technique publication-title: Swarm Evol. Comput. – year: 2021 ident: b34 article-title: Action command encoding for surrogate assisted neural architecture search publication-title: IEEE Trans. Cogn. Dev. Syst. – volume: 7 start-page: 174 year: 2003 end-page: 188 ident: b53 article-title: The balance between proximity and diversity in multiobjective evolutionary algorithms publication-title: IEEE Trans. Evol. Comput. – year: 2022 ident: b10 article-title: Integrating conjugate gradients into evolutionary algorithms for large-scale continuous multi-objective optimization publication-title: IEEE/CAA J. Autom. Sin. – volume: 77 year: 2023 ident: b16 article-title: Surrogate-assisted multi-objective optimization via knee-oriented Pareto front estimation publication-title: Swarm Evol. Comput. – volume: 77 year: 2023 ident: b2 article-title: Decomposition-based multi-objective evolutionary algorithm for virtual machine and task joint scheduling of cloud computing in data space publication-title: Swarm Evol. Comput. – volume: 22 start-page: 129 year: 2016 end-page: 142 ident: b21 article-title: A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization publication-title: IEEE Trans. Evol. Comput. – year: 2022 ident: b29 article-title: Expensive multiobjective optimization by relation learning and prediction publication-title: IEEE Trans. Evol. Comput. – volume: 6 start-page: 182 year: 2002 end-page: 197 ident: b4 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Trans. Evol. Comput. – volume: 11 start-page: 712 year: 2007 end-page: 731 ident: b8 article-title: MOEA/D: A multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Trans. Evol. Comput. – volume: 60 year: 2021 ident: b30 article-title: Two infill criteria driven surrogate-assisted multi-objective evolutionary algorithms for computationally expensive problems with medium dimensions publication-title: Swarm Evol. Comput. – reference: E. Zitzler, M. Laumanns, L. Thiele, SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization, in: Proceedings of the Fifth Conference on Evolutionary Methods for Design, Optimization and Control with Applications To Industrial Problems, 2001, pp. 95–100. – volume: 75 year: 2022 ident: b28 article-title: Comparative study of regression vs pairwise models for surrogate-based heuristic optimisation publication-title: Swarm Evol. Comput. – year: 2021 ident: b35 article-title: Expensive multi-objective evolutionary optimization assisted by dominance prediction publication-title: IEEE Trans. Evol. Comput. – volume: 518 start-page: 190 year: 2023 end-page: 205 ident: b45 article-title: A practical tutorial on solving optimization problems via PlatEMO publication-title: Neurocomputing – volume: 20 start-page: 273 year: 1995 end-page: 297 ident: b18 article-title: Support-vector networks publication-title: Mach. Learn. – volume: 23 start-page: 74 year: 2018 end-page: 88 ident: b24 article-title: A classification-based surrogate-assisted evolutionary algorithm for expensive many-objective optimization publication-title: IEEE Trans. Evol. Comput. – volume: 25 start-page: 2450 year: 2020 end-page: 2469 ident: b33 article-title: A classification-based surrogate-assisted multiobjective evolutionary algorithm for production optimization under geological uncertainty publication-title: SPE J. – reference: S. Kukkonen, J. Lampinen, GDE3: The third evolution step of generalized differential evolution, in: Proceedings of the 2005 IEEE Congress on Evolutionary Computation, 2005, pp. 443–450. – volume: 22 start-page: 609 year: 2018 end-page: 622 ident: b9 article-title: An indicator based multi-objective evolutionary algorithm with reference point adaptation for better versatility publication-title: IEEE Trans. Evol. Comput. – reference: J. Zhang, A. Zhou, G. Zhang, A classification and Pareto domination based multiobjective evolutionary algorithm, in: Proceedings of the 2015 IEEE Congress on Evolutionary Computation, 2015, pp. 2883–2890. – year: 2021 ident: b22 article-title: Evolutionary optimization of high-dimensional multiobjective and many-objective expensive problems assisted by a dropout neural network publication-title: IEEE Trans. Syst. Man Cybern.: Systems – volume: 21 start-page: 898 year: 2017 end-page: 913 ident: b26 article-title: Expected improvement of penalty-based boundary intersection for expensive multiobjective optimization publication-title: IEEE Trans. Evol. Comput. – volume: 9 start-page: 115 year: 1995 end-page: 148 ident: b42 article-title: Simulated binary crossover for continuous search space publication-title: Complex Syst. – volume: 3 start-page: 67 year: 2017 end-page: 81 ident: b49 article-title: A benchmark test suite for evolutionary many-objective optimization publication-title: Complex Intell. Syst. – volume: 25 start-page: 405 year: 2021 end-page: 418 ident: b59 article-title: A multi-population evolutionary algorithm for solving large-scale multi-modal multi-objective optimization problems publication-title: IEEE Trans. Evol. Comput. – volume: 26 start-page: 30 year: 1996 end-page: 45 ident: b43 article-title: A combined genetic adaptive search (GeneAS) for engineering design publication-title: Comput. Sci. Inform. – volume: 23 start-page: 442 year: 2019 end-page: 458 ident: b15 article-title: Data-driven evolutionary optimization: An overview and case studies publication-title: IEEE Trans. Evol. Comput. – volume: 18 start-page: 180 year: 2013 end-page: 192 ident: b27 article-title: A Gaussian process surrogate model assisted evolutionary algorithm for medium scale expensive optimization problems publication-title: IEEE Trans. Evol. Comput. – reference: Y. Tian, X. Xiang, X. Zhang, R. Cheng, Y. Jin, Sampling reference points on the Pareto fronts of benchmark multi-objective optimization problems, in: Proceedings of the 2018 IEEE Congress on Evolutionary Computation, 2018. – volume: 75 year: 2022 ident: b25 article-title: An approach for computationally expensive multi-objective optimization problems with independently evaluable objectives publication-title: Swarm Evol. Comput. – volume: 538 year: 2023 ident: b58 article-title: An adaptive Gaussian process based manifold transfer learning to expensive dynamic multi-objective optimization publication-title: Neurocomputing – volume: 116 year: 2020 ident: b14 article-title: A survey on kriging-based infill algorithms for multiobjective simulation optimization publication-title: Comput. Oper. Res. – reference: C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, G. Hullender, Learning to rank using gradient descent, in: Proceedings of the 22nd International Conference on Machine Learning, 2005, pp. 89–96. – volume: 19 start-page: 201 year: 2015 end-page: 213 ident: b7 article-title: An efficient approach to non-dominated sorting for evolutionary multi-objective optimization publication-title: IEEE Trans. Evol. Comput. – volume: 3 start-page: 257 year: 1999 end-page: 271 ident: b55 article-title: Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach publication-title: IEEE Trans. Evol. Comput. – reference: R. Eberhart, J. Kennedy, A new optimizer using particle swarm theory, in: Proceedings of the 6th International Symposium on Micro Machine and Human Science, 1995, pp. 39–43. – volume: 18 start-page: 348 year: 2013 end-page: 365 ident: b40 article-title: Shift-based density estimation for Pareto-based algorithms in many-objective optimization publication-title: IEEE Trans. Evol. Comput. – start-page: 301 year: 1996 end-page: 344 ident: b38 article-title: Fuzzy Logic and Neural Network Handbook – volume: 29 start-page: 581 year: 2017 end-page: 596 ident: b46 article-title: SOCEMO: Surrogate optimization of computationally expensive multiobjective problems publication-title: INFORMS J. Comput. – volume: 3 start-page: 106 year: 2019 end-page: 116 ident: b39 article-title: A surrogate-assisted multiobjective evolutionary algorithm for large-scale task-oriented pattern mining publication-title: IEEE Trans. Emerg. Top. Comput. Intell. – volume: 12 start-page: 73 year: 2017 end-page: 87 ident: b44 article-title: PlatEMO: A MATLAB platform for evolutionary multi-objective optimization [educational forum] publication-title: IEEE Comput. Intell. Mag. – volume: 75 year: 2022 ident: b1 article-title: Multi-objective interval portfolio optimization modeling and solving for margin trading publication-title: Swarm Evol. Comput. – volume: 1 start-page: 32 year: 2011 end-page: 49 ident: b3 article-title: Multiobjective evolutionary algorithms: A survey of the state of the art publication-title: Swarm Evol. Comput. – volume: 50 start-page: 3696 year: 2020 end-page: 3708 ident: b41 article-title: Efficient large-scale multiobjective optimization based on a competitive swarm optimizer publication-title: IEEE Trans. Cybern. – reference: I. Steponavičė, R.J. Hyndman, K. Smith-Miles, L. Villanova, Efficient identification of the Pareto optimal set, in: Proceedings of the 2014 International Conference on Learning and Intelligent Optimization, 2014, pp. 341–352. – volume: 38 start-page: W615 issue: suppl_2 year: 2010 ident: 10.1016/j.swevo.2023.101323_b11 article-title: e-LEA3D: A computational-aided drug design web server publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkq322 – start-page: 105 year: 2005 ident: 10.1016/j.swevo.2023.101323_b47 article-title: Scalable test problems for evolutionary multiobjective optimization – volume: 77 year: 2023 ident: 10.1016/j.swevo.2023.101323_b2 article-title: Decomposition-based multi-objective evolutionary algorithm for virtual machine and task joint scheduling of cloud computing in data space publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2023.101230 – volume: 22 start-page: 609 issue: 4 year: 2018 ident: 10.1016/j.swevo.2023.101323_b9 article-title: An indicator based multi-objective evolutionary algorithm with reference point adaptation for better versatility publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2017.2749619 – ident: 10.1016/j.swevo.2023.101323_b32 doi: 10.1007/978-3-319-09584-4_29 – year: 2021 ident: 10.1016/j.swevo.2023.101323_b35 article-title: Expensive multi-objective evolutionary optimization assisted by dominance prediction publication-title: IEEE Trans. Evol. Comput. – year: 2022 ident: 10.1016/j.swevo.2023.101323_b10 article-title: Integrating conjugate gradients into evolutionary algorithms for large-scale continuous multi-objective optimization publication-title: IEEE/CAA J. Autom. Sin. doi: 10.1109/JAS.2022.105875 – ident: 10.1016/j.swevo.2023.101323_b23 doi: 10.1109/CEC.2015.7257247 – volume: 3 start-page: 67 issue: 1 year: 2017 ident: 10.1016/j.swevo.2023.101323_b49 article-title: A benchmark test suite for evolutionary many-objective optimization publication-title: Complex Intell. Syst. doi: 10.1007/s40747-017-0039-7 – year: 2020 ident: 10.1016/j.swevo.2023.101323_b56 – volume: 22 start-page: 129 issue: 1 year: 2016 ident: 10.1016/j.swevo.2023.101323_b21 article-title: A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2016.2622301 – volume: 18 start-page: 348 issue: 3 year: 2013 ident: 10.1016/j.swevo.2023.101323_b40 article-title: Shift-based density estimation for Pareto-based algorithms in many-objective optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2013.2262178 – volume: 75 year: 2022 ident: 10.1016/j.swevo.2023.101323_b1 article-title: Multi-objective interval portfolio optimization modeling and solving for margin trading publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2022.101141 – volume: 20 start-page: 273 issue: 3 year: 1995 ident: 10.1016/j.swevo.2023.101323_b18 article-title: Support-vector networks publication-title: Mach. Learn. doi: 10.1023/A:1022627411411 – ident: 10.1016/j.swevo.2023.101323_b51 doi: 10.2514/6.1996-4161 – volume: 7 start-page: 174 issue: 2 year: 2003 ident: 10.1016/j.swevo.2023.101323_b53 article-title: The balance between proximity and diversity in multiobjective evolutionary algorithms publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2003.810761 – volume: 1 start-page: 32 year: 2011 ident: 10.1016/j.swevo.2023.101323_b3 article-title: Multiobjective evolutionary algorithms: A survey of the state of the art publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2011.03.001 – ident: 10.1016/j.swevo.2023.101323_b17 doi: 10.1109/CEC.2005.1555050 – volume: 23 start-page: 3137 year: 2019 ident: 10.1016/j.swevo.2023.101323_b13 article-title: A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms publication-title: Soft Comput. doi: 10.1007/s00500-017-2965-0 – volume: 50 start-page: 3696 issue: 8 year: 2020 ident: 10.1016/j.swevo.2023.101323_b41 article-title: Efficient large-scale multiobjective optimization based on a competitive swarm optimizer publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2019.2906383 – volume: 26 start-page: 30 year: 1996 ident: 10.1016/j.swevo.2023.101323_b43 article-title: A combined genetic adaptive search (GeneAS) for engineering design publication-title: Comput. Sci. Inform. – volume: 75 year: 2022 ident: 10.1016/j.swevo.2023.101323_b28 article-title: Comparative study of regression vs pairwise models for surrogate-based heuristic optimisation publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2022.101176 – volume: 9 start-page: 115 issue: 2 year: 1995 ident: 10.1016/j.swevo.2023.101323_b42 article-title: Simulated binary crossover for continuous search space publication-title: Complex Syst. – volume: 10 start-page: 477 issue: 5 year: 2006 ident: 10.1016/j.swevo.2023.101323_b48 article-title: A review of multiobjective test problems and a scalable test problem toolkit publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2005.861417 – volume: 23 start-page: 442 issue: 3 year: 2019 ident: 10.1016/j.swevo.2023.101323_b15 article-title: Data-driven evolutionary optimization: An overview and case studies publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2018.2869001 – ident: 10.1016/j.swevo.2023.101323_b54 doi: 10.1109/CEC.2018.8477730 – ident: 10.1016/j.swevo.2023.101323_b5 doi: 10.1109/CEC.2005.1554717 – volume: 49 start-page: 1012 issue: 3 year: 2018 ident: 10.1016/j.swevo.2023.101323_b31 article-title: Heterogeneous ensemble-based infill criterion for evolutionary multiobjective optimization of expensive problems publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2018.2794503 – year: 2022 ident: 10.1016/j.swevo.2023.101323_b29 article-title: Expensive multiobjective optimization by relation learning and prediction publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2022.3152582 – volume: 518 start-page: 190 year: 2023 ident: 10.1016/j.swevo.2023.101323_b45 article-title: A practical tutorial on solving optimization problems via PlatEMO publication-title: Neurocomputing doi: 10.1016/j.neucom.2022.10.075 – volume: 6 start-page: 182 issue: 2 year: 2002 ident: 10.1016/j.swevo.2023.101323_b4 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.996017 – volume: 60 year: 2021 ident: 10.1016/j.swevo.2023.101323_b30 article-title: Two infill criteria driven surrogate-assisted multi-objective evolutionary algorithms for computationally expensive problems with medium dimensions publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2020.100774 – ident: 10.1016/j.swevo.2023.101323_b37 – volume: 12 start-page: 73 issue: 4 year: 2017 ident: 10.1016/j.swevo.2023.101323_b44 article-title: PlatEMO: A MATLAB platform for evolutionary multi-objective optimization [educational forum] publication-title: IEEE Comput. Intell. Mag. doi: 10.1109/MCI.2017.2742868 – volume: 4 start-page: 62 issue: 3 year: 2009 ident: 10.1016/j.swevo.2023.101323_b12 article-title: A systems approach to evolutionary multiobjective structural optimization and beyond publication-title: IEEE Comput. Intell. Mag. doi: 10.1109/MCI.2009.933094 – volume: 42 start-page: 55 issue: 1 year: 2000 ident: 10.1016/j.swevo.2023.101323_b36 article-title: A comparison of three methods for selecting values of input variables in the analysis of output from a computer code publication-title: Technometrics doi: 10.1080/00401706.2000.10485979 – volume: 3 start-page: 257 issue: 4 year: 1999 ident: 10.1016/j.swevo.2023.101323_b55 article-title: Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.797969 – volume: 72 year: 2022 ident: 10.1016/j.swevo.2023.101323_b20 article-title: A convolutional neural network-based surrogate model for multi-objective optimization evolutionary algorithm based on decomposition publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2022.101081 – volume: 23 start-page: 74 issue: 1 year: 2018 ident: 10.1016/j.swevo.2023.101323_b24 article-title: A classification-based surrogate-assisted evolutionary algorithm for expensive many-objective optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2018.2802784 – volume: 33 start-page: 399 issue: 3 year: 2001 ident: 10.1016/j.swevo.2023.101323_b52 article-title: An evolutionary algorithm for multiobjective optimization publication-title: Eng. Optim. doi: 10.1080/03052150108940926 – volume: 21 start-page: 898 issue: 6 year: 2017 ident: 10.1016/j.swevo.2023.101323_b26 article-title: Expected improvement of penalty-based boundary intersection for expensive multiobjective optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2017.2693320 – volume: 77 year: 2023 ident: 10.1016/j.swevo.2023.101323_b16 article-title: Surrogate-assisted multi-objective optimization via knee-oriented Pareto front estimation publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2023.101252 – year: 2021 ident: 10.1016/j.swevo.2023.101323_b22 article-title: Evolutionary optimization of high-dimensional multiobjective and many-objective expensive problems assisted by a dropout neural network publication-title: IEEE Trans. Syst. Man Cybern.: Systems – volume: 25 start-page: 2450 issue: 05 year: 2020 ident: 10.1016/j.swevo.2023.101323_b33 article-title: A classification-based surrogate-assisted multiobjective evolutionary algorithm for production optimization under geological uncertainty publication-title: SPE J. doi: 10.2118/201229-PA – ident: 10.1016/j.swevo.2023.101323_b6 doi: 10.1109/MHS.1995.494215 – volume: 11 start-page: 712 issue: 6 year: 2007 ident: 10.1016/j.swevo.2023.101323_b8 article-title: MOEA/D: A multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2007.892759 – volume: 75 year: 2022 ident: 10.1016/j.swevo.2023.101323_b25 article-title: An approach for computationally expensive multi-objective optimization problems with independently evaluable objectives publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2022.101146 – volume: 3 start-page: 106 issue: 2 year: 2019 ident: 10.1016/j.swevo.2023.101323_b39 article-title: A surrogate-assisted multiobjective evolutionary algorithm for large-scale task-oriented pattern mining publication-title: IEEE Trans. Emerg. Top. Comput. Intell. doi: 10.1109/TETCI.2018.2872055 – volume: 18 start-page: 602 issue: 4 year: 2014 ident: 10.1016/j.swevo.2023.101323_b50 article-title: An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part II: Handling constraints and extending to an adaptive approach publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2013.2281534 – volume: 538 year: 2023 ident: 10.1016/j.swevo.2023.101323_b58 article-title: An adaptive Gaussian process based manifold transfer learning to expensive dynamic multi-objective optimization publication-title: Neurocomputing doi: 10.1016/j.neucom.2023.03.073 – volume: 19 start-page: 201 issue: 2 year: 2015 ident: 10.1016/j.swevo.2023.101323_b7 article-title: An efficient approach to non-dominated sorting for evolutionary multi-objective optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2014.2308305 – volume: 116 year: 2020 ident: 10.1016/j.swevo.2023.101323_b14 article-title: A survey on kriging-based infill algorithms for multiobjective simulation optimization publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2019.104869 – start-page: 301 year: 1996 ident: 10.1016/j.swevo.2023.101323_b38 – volume: 29 start-page: 581 issue: 4 year: 2017 ident: 10.1016/j.swevo.2023.101323_b46 article-title: SOCEMO: Surrogate optimization of computationally expensive multiobjective problems publication-title: INFORMS J. Comput. doi: 10.1287/ijoc.2017.0749 – volume: 25 start-page: 405 issue: 3 year: 2021 ident: 10.1016/j.swevo.2023.101323_b59 article-title: A multi-population evolutionary algorithm for solving large-scale multi-modal multi-objective optimization problems publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2020.3044711 – volume: 75 year: 2022 ident: 10.1016/j.swevo.2023.101323_b19 article-title: Adaptive surrogate-assisted multi-objective evolutionary algorithm using an efficient infill technique publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2022.101170 – volume: 18 start-page: 180 issue: 2 year: 2013 ident: 10.1016/j.swevo.2023.101323_b27 article-title: A Gaussian process surrogate model assisted evolutionary algorithm for medium scale expensive optimization problems publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2013.2248012 – ident: 10.1016/j.swevo.2023.101323_b57 doi: 10.1145/1102351.1102363 – year: 2021 ident: 10.1016/j.swevo.2023.101323_b34 article-title: Action command encoding for surrogate assisted neural architecture search publication-title: IEEE Trans. Cogn. Dev. Syst. |
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