An effective surrogate model assisted algorithm for multi-objective optimization: application to wind farm layout design
Due to the intricate and diverse nature of industrial systems, traditional optimization algorithms require a significant amount of time to search for the optimal solution throughout the entire design space, making them unsuitable for meeting practical industrial demands. To address this issue, we pr...
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| Vydané v: | Frontiers in energy research Ročník 11 |
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Frontiers Media S.A
14.09.2023
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| ISSN: | 2296-598X, 2296-598X |
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| Abstract | Due to the intricate and diverse nature of industrial systems, traditional optimization algorithms require a significant amount of time to search for the optimal solution throughout the entire design space, making them unsuitable for meeting practical industrial demands. To address this issue, we propose a novel approach that combines surrogate models with optimization algorithms. Firstly, we introduce the Sparse Gaussian Process regression (SGP) into the surrogate model, proposing the SGP surrogate-assisted optimization method. This approach effectively overcomes the computational expense caused by the large amount of data required in Gaussian Process model. Secondly, we use grid partitioning to divide the optimization problem into multiple regions, and utilize the multi-objective particle swarm optimization algorithm to optimize particles in each region. By combining the advantages of grid partitioning and particle swarm optimization, which overcome the limitations of traditional optimization algorithms in handling multi-objective problems. Lastly, the effectiveness and robustness of the proposed method are verified through three types of 12 test functions and a wind farm layout optimization case study. The results show that the combination of meshing and SGP surrogate enables more accurate identification of optimal solutions, thereby improving the accuracy and speed of the optimization results. Additionally, the method demonstrates its applicability to a variety of complex multi-objective optimization problems. |
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| AbstractList | Due to the intricate and diverse nature of industrial systems, traditional optimization algorithms require a significant amount of time to search for the optimal solution throughout the entire design space, making them unsuitable for meeting practical industrial demands. To address this issue, we propose a novel approach that combines surrogate models with optimization algorithms. Firstly, we introduce the Sparse Gaussian Process regression (SGP) into the surrogate model, proposing the SGP surrogate-assisted optimization method. This approach effectively overcomes the computational expense caused by the large amount of data required in Gaussian Process model. Secondly, we use grid partitioning to divide the optimization problem into multiple regions, and utilize the multi-objective particle swarm optimization algorithm to optimize particles in each region. By combining the advantages of grid partitioning and particle swarm optimization, which overcome the limitations of traditional optimization algorithms in handling multi-objective problems. Lastly, the effectiveness and robustness of the proposed method are verified through three types of 12 test functions and a wind farm layout optimization case study. The results show that the combination of meshing and SGP surrogate enables more accurate identification of optimal solutions, thereby improving the accuracy and speed of the optimization results. Additionally, the method demonstrates its applicability to a variety of complex multi-objective optimization problems. |
| Author | Chen, Yong Wang, Li Huang, Hui |
| Author_xml | – sequence: 1 givenname: Yong surname: Chen fullname: Chen, Yong – sequence: 2 givenname: Li surname: Wang fullname: Wang, Li – sequence: 3 givenname: Hui surname: Huang fullname: Huang, Hui |
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| Cites_doi | 10.1016/j.knosys.2021.107049 10.1109/tsmc.2023.3257030 10.1016/j.strusafe.2017.06.003 10.1007/s11081-017-9370-5 10.1109/tcad.2015.2501307 10.1109/tevc.2013.2248012 10.1016/j.apenergy.2013.08.061 10.1016/j.ijepes.2021.107401 10.1080/01621459.1991.10475138 10.1016/j.compfluid.2022.105643 10.2514/1.j058807 10.1016/j.joim.2021.11.008 10.2514/1.c10485e 10.1016/j.compchemeng.2015.08.022 10.1007/s00521-022-07705-4 10.1109/tevc.2004.826067 10.4018/978-1-59904-498-9.ch002 10.1016/j.eswa.2022.119495 10.1109/tsmcc.2005.855506 10.1016/j.asoc.2021.108353 10.1109/tevc.2017.2675628 10.3390/app11031213 10.1016/j.apenergy.2019.04.047 10.1007/s00500-022-07362-8 10.1016/j.cma.2020.113269 10.48550/arXiv.2105.03893 10.1016/j.renene.2021.02.003 10.1109/access.2018.2832181 10.1007/s40747-022-00717-6 10.1016/j.oceaneng.2021.110239 10.1016/j.swevo.2016.12.005 10.1007/s11047-022-09907-0 10.1016/j.biosystems.2019.05.005 10.1016/j.apenergy.2021.117286 10.1038/nature14541 10.1007/s00500-015-1767-5 10.1023/a:1008306431147 10.3390/sym14061219 10.1016/j.jocs.2015.11.004 10.1155/2021/6681489 10.1016/j.engstruct.2023.116495 10.1016/j.energy.2020.119214 10.1155/2022/4179898 10.1016/j.energy.2017.02.174 |
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| References | Han (B13) 2012; 343 Liu (B24) 2013; 18 Wang (B42); 14 Parsopoulos (B34) 2008 Preen (B35) 2019; 182 Vafadar (B41) 2021; 11 Talgorn (B40) 2018; 19 Ciccazzo (B4) 2015; 35 Ling (B22) 2022; 20 Zhao (B46) 2022; 246 Sun (B39) 2017; 21 Akinola (B1) 2022; 34 Jones (B16) 1998; 13 Liu (B23) 2016; 12 Joseph (B17) 2008 Gu (B12) 2021; 223 Satria Palar (B36) 2020; 58 Cui (B6) 2017; 125 Mavrovouniotis (B30) 2017; 33 Li (B20) 2015 Currin (B7) 1991; 86 Palmer (B33) 2019 Zhang (B45) 2019; 247 Giovanis (B9) 2020; 370 Zheng (B47) 2022; 8 Kudela (B18) 2022; 26 Li (B19) 2018; 6 Su (B38) 2017; 68 Jeong (B15) 2005; 42 Zhou (B48) 2006; 37 He (B14) 2023; 217 Lim (B21) 2015; 19 Liu (B26); 53 Liu (B27) 2021; 2021 Coello (B5) 2004; 8 Liu (B25); 22 Lystad (B29) 2023; 292 Shadab (B37) 2022; 134 Golparvar (B10) 2021; 299 Ghahramani (B8) 2015; 521 Wang (B43); 2022 Nguyen (B32) 2014; 113 Avendaño-Valencia (B2) 2021; 170 Grimstad (B11) 2016; 84 Moreno (B31) 2021; 216 Chen (B3) 2022; 116 Liu (B28) 2022; 243 Yang (B44) 2008; 20 |
| References_xml | – volume-title: Evolutionary algorithms and computational methods for derivatives pricing: Ucl year: 2019 ident: B33 – volume: 223 start-page: 107049 year: 2021 ident: B12 article-title: A surrogate-assisted multi-objective particle swarm optimization of expensive constrained combinatorial optimization problems publication-title: Knowledge-Based Syst. doi: 10.1016/j.knosys.2021.107049 – volume: 53 start-page: 4843 ident: B26 article-title: Solving highly expensive optimization problems via evolutionary expected improvement publication-title: IEEE Trans. Syst. Man, Cybern. Syst. doi: 10.1109/tsmc.2023.3257030 – volume: 68 start-page: 97 year: 2017 ident: B38 article-title: A Gaussian process-based dynamic surrogate model for complex engineering structural reliability analysis publication-title: Struct. Saf. doi: 10.1016/j.strusafe.2017.06.003 – volume: 19 start-page: 213 year: 2018 ident: B40 article-title: Locally weighted regression models for surrogate-assisted design optimization publication-title: Optim. Eng. doi: 10.1007/s11081-017-9370-5 – volume: 35 start-page: 1224 year: 2015 ident: B4 article-title: A SVM surrogate model-based method for parametric yield optimization publication-title: IEEE Trans. Computer-Aided Des. Integr. Circuits Syst. doi: 10.1109/tcad.2015.2501307 – volume: 18 start-page: 180 year: 2013 ident: B24 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 – volume: 113 start-page: 1043 year: 2014 ident: B32 article-title: A review on simulation-based optimization methods applied to building performance analysis publication-title: Appl. energy doi: 10.1016/j.apenergy.2013.08.061 – volume: 134 start-page: 107401 year: 2022 ident: B37 article-title: Gaussian process surrogate model for an effective life assessment of transformer considering model and measurement uncertainties publication-title: Int. J. Electr. Power and Energy Syst. doi: 10.1016/j.ijepes.2021.107401 – volume: 86 start-page: 953 year: 1991 ident: B7 article-title: Bayesian prediction of deterministic functions, with applications to the design and analysis of computer experiments publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1991.10475138 – volume: 246 start-page: 105643 year: 2022 ident: B46 article-title: Efficient aerodynamic analysis and optimization under uncertainty using multi-fidelity polynomial chaos-Kriging surrogate model publication-title: Comput. Fluids doi: 10.1016/j.compfluid.2022.105643 – volume: 58 start-page: 1864 year: 2020 ident: B36 article-title: Gaussian process surrogate model with composite kernel learning for engineering design publication-title: AIAA J. doi: 10.2514/1.j058807 – volume: 20 start-page: 5843 year: 2008 ident: B44 article-title: Multi-objective particle swarm optimization based on adaptive grid algorithms publication-title: J. Syst. Simul. – volume: 20 start-page: 1 year: 2022 ident: B22 article-title: Complementary and alternative medicine during COVID-19 pandemic: what we have done publication-title: IEEE Trans. Reliab. doi: 10.1016/j.joim.2021.11.008 – volume: 42 start-page: 1375 year: 2005 ident: B15 article-title: Efficient optimization design method using kriging model publication-title: J. Aircr. doi: 10.2514/1.c10485e – volume: 84 start-page: 237 year: 2016 ident: B11 article-title: Global optimization of multiphase flow networks using spline surrogate models publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2015.08.022 – volume: 34 start-page: 19751 year: 2022 ident: B1 article-title: Multiclass feature selection with metaheuristic optimization algorithms: a review publication-title: Neural Comput. Appl. doi: 10.1007/s00521-022-07705-4 – volume: 8 start-page: 256 year: 2004 ident: B5 article-title: Handling multiple objectives with particle swarm optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/tevc.2004.826067 – start-page: 20 volume-title: Multi-objective optimization in computational intelligence: Theory and practice year: 2008 ident: B34 doi: 10.4018/978-1-59904-498-9.ch002 – volume: 217 start-page: 119495 year: 2023 ident: B14 article-title: A review of surrogate-assisted evolutionary algorithms for expensive optimization problems publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2022.119495 – volume-title: Blind kriging: A new method for developing metamodels year: 2008 ident: B17 – volume: 37 start-page: 66 year: 2006 ident: B48 article-title: Combining global and local surrogate models to accelerate evolutionary optimization publication-title: IEEE Trans. Syst. Man, Cybern. Part C Appl. Rev. doi: 10.1109/tsmcc.2005.855506 – volume: 116 start-page: 108353 year: 2022 ident: B3 article-title: A radial basis function surrogate model assisted evolutionary algorithm for high-dimensional expensive optimization problems publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2021.108353 – volume: 21 start-page: 644 year: 2017 ident: B39 article-title: Surrogate-assisted cooperative swarm optimization of high-dimensional expensive problems publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/tevc.2017.2675628 – volume: 11 start-page: 1213 year: 2021 ident: B41 article-title: Advances in metal additive manufacturing: A review of common processes, industrial applications, and current challenges publication-title: Appl. Sci. doi: 10.3390/app11031213 – start-page: 541 year: 2015 ident: B20 article-title: A method for distributing reference points uniformly along the Pareto front of DTLZ test functions in many-objective evolutionary optimization – volume: 247 start-page: 270 year: 2019 ident: B45 article-title: Wind speed prediction method using shared weight long short-term memory network and Gaussian process regression publication-title: Appl. energy doi: 10.1016/j.apenergy.2019.04.047 – volume: 26 start-page: 13709 year: 2022 ident: B18 article-title: Recent advances and applications of surrogate models for finite element method computations: A review publication-title: Soft Comput. doi: 10.1007/s00500-022-07362-8 – volume: 370 start-page: 113269 year: 2020 ident: B9 article-title: Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2020.113269 – volume: 343 year: 2012 ident: B13 article-title: Surrogate-based optimization publication-title: Real-world Appl. Genet. algorithms doi: 10.48550/arXiv.2105.03893 – volume: 170 start-page: 539 year: 2021 ident: B2 article-title: Virtual fatigue diagnostics of wake-affected wind turbine via Gaussian Process Regression publication-title: Renew. Energy doi: 10.1016/j.renene.2021.02.003 – volume: 6 start-page: 26194 year: 2018 ident: B19 article-title: Evolutionary many-objective optimization: A comparative study of the state-of-the-art publication-title: IEEE Access doi: 10.1109/access.2018.2832181 – volume: 8 start-page: 4339 year: 2022 ident: B47 article-title: An adaptive model switch-based surrogate-assisted evolutionary algorithm for noisy expensive multi-objective optimization publication-title: Complex and Intelligent Syst. doi: 10.1007/s40747-022-00717-6 – volume: 243 start-page: 110239 year: 2022 ident: B28 article-title: Multi-fidelity Co-Kriging surrogate model for ship hull form optimization publication-title: Ocean. Eng. doi: 10.1016/j.oceaneng.2021.110239 – volume: 33 start-page: 1 year: 2017 ident: B30 article-title: A survey of swarm intelligence for dynamic optimization: algorithms and applications publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2016.12.005 – volume: 22 start-page: 329 ident: B25 article-title: MOEA/D with gradient-enhanced kriging for expensive multiobjective optimization publication-title: Nat. Comput. doi: 10.1007/s11047-022-09907-0 – volume: 182 start-page: 1 year: 2019 ident: B35 article-title: Towards an evolvable cancer treatment simulator publication-title: Biosystems doi: 10.1016/j.biosystems.2019.05.005 – volume: 299 start-page: 117286 year: 2021 ident: B10 article-title: A surrogate-model-based approach for estimating the first and second-order moments of offshore wind power publication-title: Appl. Energy doi: 10.1016/j.apenergy.2021.117286 – volume: 521 start-page: 452 year: 2015 ident: B8 article-title: Probabilistic machine learning and artificial intelligence publication-title: Nature doi: 10.1038/nature14541 – volume: 19 start-page: 3571 year: 2015 ident: B21 article-title: Kursawe and ZDT functions optimization using hybrid micro genetic algorithm (HMGA) publication-title: Soft Comput. doi: 10.1007/s00500-015-1767-5 – volume: 13 start-page: 455 year: 1998 ident: B16 article-title: Efficient global optimization of expensive black-box functions publication-title: J. Glob. Optim. doi: 10.1023/a:1008306431147 – volume: 14 start-page: 1219 ident: B42 article-title: Recent advances in surrogate modeling methods for uncertainty quantification and propagation publication-title: Symmetry doi: 10.3390/sym14061219 – volume: 12 start-page: 28 year: 2016 ident: B23 article-title: A multi-fidelity surrogate-model-assisted evolutionary algorithm for computationally expensive optimization problems publication-title: J. Comput. Sci. doi: 10.1016/j.jocs.2015.11.004 – volume: 2021 start-page: 1 year: 2021 ident: B27 article-title: Intelligent extremum surrogate modeling framework for dynamic probabilistic analysis of complex mechanism publication-title: Math. Problems Eng. doi: 10.1155/2021/6681489 – volume: 292 start-page: 116495 year: 2023 ident: B29 article-title: Full long-term extreme buffeting response calculations using sequential Gaussian process surrogate modeling publication-title: Eng. Struct. doi: 10.1016/j.engstruct.2023.116495 – volume: 216 start-page: 119214 year: 2021 ident: B31 article-title: Multi-objective lightning search algorithm applied to wind farm layout optimization publication-title: Energy doi: 10.1016/j.energy.2020.119214 – volume: 2022 start-page: 1 ident: B43 article-title: Fatigue optimization of structural parameters for orthotropic steel bridge decks using RSM and NSGA-II publication-title: Math. Problems Eng. doi: 10.1155/2022/4179898 – volume: 125 start-page: 681 year: 2017 ident: B6 article-title: Review: multi-objective optimization methods and application in energy saving publication-title: Energy doi: 10.1016/j.energy.2017.02.174 |
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