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