Adaptive surrogate-assisted multi-objective evolutionary algorithm using an efficient infill technique

Swarm and Evolutionary Computation - Tập 75 - Trang 101170 - 2022
Mengtian Wu1,2, Lingling Wang1,2, Jin Xu1,3, Pengjie Hu4,2, Pengcheng Xu1,2
1State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China
2College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, China
3College of Agricultural Science and Engineering, Hohai University, Nanjing, China
4State Key Laboratory of Hydrology Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China

Tài liệu tham khảo

Deb, 2002, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., 6, 182, 10.1109/4235.996017 Zhang, 2007, MOEA/D: A multiobjective evolutionary algorithm based on decomposition, IEEE Trans. Evol. Comput., 11, 712, 10.1109/TEVC.2007.892759 Han, 2020, Discrete evolutionary multi-objective optimization for energy-efficient blocking flow shop scheduling with setup time, Appl. Soft. Comput., 93, 10.1016/j.asoc.2020.106343 Liu, 2020, Handling imbalance between convergence and diversity in the decision space in evolutionary multimodal multiobjective optimization, IEEE Trans. Evol. Comput., 24, 551 Jin, 2018, Data-driven evolutionary optimization: an overview and case studies, IEEE Trans. Evol. Comput., 23, 442, 10.1109/TEVC.2018.2869001 Jin, 2011, Surrogate-assisted evolutionary computation: recent advances and future challenges, Swarm Evol. Comput., 1, 61, 10.1016/j.swevo.2011.05.001 Gong, 2016, Multiobjective adaptive surrogate modeling-based optimization for parameter estimation of large, complex geophysical models, Water Resour. Res., 52, 1984, 10.1002/2015WR018230 Huang, 2006, Sequential kriging optimization using multiple-fidelity evaluations, Struct. Multidiscip. Optim., 32, 369, 10.1007/s00158-005-0587-0 Shyy, 2010, Recent progress in flapping wing aerodynamics and aeroelasticity, Prog. Aeosp. Sci., 46, 284, 10.1016/j.paerosci.2010.01.001 Pan, 2018, A classification-based surrogate-assisted evolutionary algorithm for expensive many-objective optimization, IEEE Trans. Evol. Comput., 23, 74, 10.1109/TEVC.2018.2802784 Stork, 2020, Open issues in surrogate-assisted optimization, in:, 225 Jones, 1998, Efficient global optimization of expensive black-box functions, J. Glob. Optim., 13, 455, 10.1023/A:1008306431147 Broomhead, 1988 Clarke, 2005, Analysis of support vector regression for approximation of complex engineering analyses, J. Mech. Des., 10.1115/1.1897403 Mitchell, 1997, Artificial neural networks, Mach. Learn., 45, 81 Chugh, 2019, A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms, Soft Comput., 23, 3137, 10.1007/s00500-017-2965-0 Díaz-Manríquez, 2011, On the selection of surrogate models in evolutionary optimization algorithms, 2155 Montemayor-Garcia, 2011, A study of surrogate models for their use in multiobjective evolutionary algorithms, 1 Lin, 2021, An ensemble surrogate-based framework for expensive multiobjective evolutionary optimization, IEEE Trans. Evol. Comput., 26, 631, 10.1109/TEVC.2021.3103936 Rosales-Pérez, 2013, A hybrid surrogate-based approach for evolutionary multi-objective optimization, 2548 Kattan, 2012, Evolving radial basis function networks via GP for estimating fitness values using surrogate models Sun, 2017, Surrogate-assisted cooperative swarm optimization of high-dimensional expensive problems, IEEE Trans. Evol. Comput., 21, 644, 10.1109/TEVC.2017.2675628 Li, 2020, A surrogate-assisted multiswarm optimization algorithm for high-dimensional computationally expensive problems, IEEE Trans. Cybern., 51, 1390, 10.1109/TCYB.2020.2967553 Dong, 2020, Surrogate-assisted grey wolf optimization for high-dimensional, computationally expensive black-box problems, Swarm Evol. Comput., 57, 10.1016/j.swevo.2020.100713 Li, 2021, Two infill criteria driven surrogate-assisted multi-objective evolutionary algorithms for computationally expensive problems with medium dimensions, Swarm Evol. Comput., 60, 10.1016/j.swevo.2020.100774 Dong, 2021, Surrogate-guided multi-objective optimization (SGMOO) using an efficient online sampling strategy, Knowl. Based Syst., 220, 10.1016/j.knosys.2021.106919 Wolpert, 1997, No free lunch theorems for optimization, IEEE Trans. Evol. Comput., 1, 67, 10.1109/4235.585893 Ho, 2002, Simple explanation of the no-free-lunch theorem and its implications, J. Optim. Theory Appl., 115, 549, 10.1023/A:1021251113462 Yu, 2022, A twofold infill criterion-driven heterogeneous ensemble surrogate-assisted evolutionary algorithm for computationally expensive problems, Knowl. Based Syst., 236, 10.1016/j.knosys.2021.107747 Wang, 2017, Committee-based active learning for surrogate-assisted particle swarm optimization of expensive problems, IEEE Trans. Cybern., 47, 2664, 10.1109/TCYB.2017.2710978 Chugh, 2016, A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization, IEEE Trans. Evol. Comput., 22, 129, 10.1109/TEVC.2016.2622301 Zhang, 2009, Expensive multiobjective optimization by MOEA/D with Gaussian process model, IEEE Trans. Evol. Comput., 14, 456, 10.1109/TEVC.2009.2033671 Mitra, 2011, Successive approximate model based multi-objective optimization for an industrial straight grate iron ore induration process using evolutionary algorithm, Chem. Eng. Sci., 66, 3471, 10.1016/j.ces.2011.03.041 Li, 2009, Improving multi-objective genetic algorithms with adaptive design of experiments and online metamodeling, Struct. Multidiscip. Optim., 37, 447, 10.1007/s00158-008-0251-6 Li, 2008, A kriging metamodel assisted multi-objective genetic algorithm for design optimization, J. Mech. Des., 10.1115/1.2829879 Deb, 2021, Surrogate modeling approaches for multiobjective optimization: methods, taxonomy, and results, Math. Comput. Appl., 26, 5 Deb, 2018, A taxonomy for metamodeling frameworks for evolutionary multiobjective optimization, IEEE Trans. Evol. Comput., 23, 104, 10.1109/TEVC.2018.2828091 Li, 2021, A comparative study of pre-screening strategies within a surrogate-assisted multi-objective algorithm framework for computationally expensive problems, Neural Comput. Appl., 33, 4387, 10.1007/s00521-020-05258-y Zhan, 2017, Expected improvement matrix-based infill criteria for expensive multiobjective optimization, IEEE Trans. Evol. Comput., 21, 956, 10.1109/TEVC.2017.2697503 Hardy, 1971, Multiquadric equations of topography and other irregular surfaces, J. Geophys. Res., 76, 1905, 10.1029/JB076i008p01905 Dyn, 1986, Numerical procedures for surface fitting of scattered data by radial functions, SIAM J. Sci. Stat. Comput., 7, 639, 10.1137/0907043 Park, 1991, Universal approximation using radial-basis-function networks, Neural Comput., 3, 246, 10.1162/neco.1991.3.2.246 Gutmann, 2001, A radial basis function method for global optimization, J. Glob. Optim., 19, 201, 10.1023/A:1011255519438 Sacks, 1989, Design and analysis of computer experiments, Stat. Sci., 4, 409 Stone, 1974, Cross-validatory choice and assessment of statistical predictions, J. R. Stat. Soc. Ser. B (Methodol.), 36, 111 Stone, 1977, An asymptotic equivalence of choice of model by cross-validation and Akaike's criterion, J. R. Stat. Soc. Ser. B (Methodol.), 39, 44 Lophaven, 2002 Wang, 2020, An adaptive Bayesian approach to surrogate-assisted evolutionary multi-objective optimization, Inf. Sci., 519, 317, 10.1016/j.ins.2020.01.048 Song, 2021, A kriging-assisted two-archive evolutionary algorithm for expensive many-objective optimization, IEEE Trans. Evol. Comput., 25, 1013, 10.1109/TEVC.2021.3073648 Guo, 2018, Heterogeneous ensemble-based infill criterion for evolutionary multiobjective optimization of expensive problems, IEEE Trans. Cybern., 49, 1012, 10.1109/TCYB.2018.2794503 Wang, 2014, Two_Arch2: An improved two-archive algorithm for many-objective optimization, IEEE Trans. Evol. Comput., 19, 524, 10.1109/TEVC.2014.2350987 Osaba, 2021, A tutorial on the design, experimentation and application of metaheuristic algorithms to real-world optimization problems, Swarm Evol. Comput., 64, 10.1016/j.swevo.2021.100888 Tian, 2017, PlatEMO: A MATLAB platform for evolutionary multi-objective optimization [educational forum], IEEE Comput. Intell. Mag., 12, 73, 10.1109/MCI.2017.2742868 Deb, 1999, Multi-objective genetic algorithms: problem difficulties and construction of test problems, Evol. Comput., 7, 205, 10.1162/evco.1999.7.3.205 Deb, 2002, Scalable multi-objective optimization test problems, 825 Huband, 2006, A review of multiobjective test problems and a scalable test problem toolkit, IEEE Trans. Evol. Comput., 10, 477, 10.1109/TEVC.2005.861417 Mohammadi, 2013, A new performance metric for user-preference based multi-objective evolutionary algorithms, 2825 He, 2020, A repository of real-world datasets for data-driven evolutionary multiobjective optimization, Complex Intell. Syst., 6, 189, 10.1007/s40747-019-00126-2