C. A. Mader, J. R. Martins, J. J. Alonso, and E. Van-der-weide, ADjoint: an approach for the rapid development of discrete adjoint solvers, AIAA J, vol.46, issue.4, pp.863-873, 2008.

J. R. Martins and J. T. Hwang, Review and unification of methods for computing derivatives of multidisciplinary computational models, AIAA J, vol.51, issue.11, pp.2582-2599, 2013.

G. K. Kenway and J. R. Martins, Multipoint high-fidelity aerostructural optimization of a transport aircraft configuration, J. Aircr, vol.51, issue.1, pp.144-160, 2014.
DOI : 10.2514/1.c032150
URL : https://deepblue.lib.umich.edu/bitstream/2027.42/140504/1/1.c032150.pdf

Z. Lyu, G. K. Kenway, and J. R. Martins, Aerodynamic shape optimization investigations of the common research model wing benchmark, AIAA J, vol.53, issue.4, pp.968-985, 2015.

Y. Yu, Z. Lyu, Z. Xu, and J. R. Martins, On the influence of optimization algorithm and starting design on wing aerodynamic shape optimization, Aerosp. Sci. Technol, vol.75, pp.183-199, 2018.

N. Bons, X. He, C. A. Mader, and J. R. Martins, Multimodality in aerodynamic wing design optimization, AIAA J, vol.57, issue.3, pp.1004-1018, 2019.
DOI : 10.2514/6.2017-3753
URL : https://deepblue.lib.umich.edu/bitstream/2027.42/143093/1/6.2017-3753.pdf

A. R. Conn, K. Scheinberg, and L. N. Vicente, Introduction to Derivative-Free Optimization, vol.8, 2009.
DOI : 10.1137/1.9780898718768

L. M. Rios and N. V. Sahinidis, Derivative-free optimization: a review of algorithms and comparison of software implementations, J. Glob. Optim, vol.56, issue.3, pp.1247-1293, 2013.

F. Boukouvala, R. Misener, and C. A. Floudas, Global optimization advances in mixedinteger nonlinear programming, MINLP, and constrained derivative-free optimization, CDFO, Eur. J. Oper. Res, vol.252, issue.3, pp.701-727, 2016.
DOI : 10.1016/j.ejor.2015.12.018
URL : https://manuscript.elsevier.com/S037722171501142X/pdf/S037722171501142X.pdf

C. Audet and W. Hare, Derivative-Free and Blackbox Optimization, 2017.
DOI : 10.1007/978-3-319-68913-5

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput, vol.6, issue.2, pp.182-197, 2002.
DOI : 10.1109/4235.996017

N. Hansen, S. D. Müller, and P. Koumoutsakos, Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES), Evol. Comput, vol.11, issue.1, pp.1-18, 2003.

N. Hansen and S. Kern, Evaluating the CMA Evolution Strategy on, Multimodal Test Functions, PPSN, vol.8, pp.282-291, 2004.
DOI : 10.1007/978-3-540-30217-9_29

G. A. Jastrebski and D. V. Arnold, Improving evolution strategies through active covariance matrix adaptation, IEEE Congress on Evolutionary Computation, pp.2814-2821, 2006.
DOI : 10.1109/cec.2006.1688662

Y. Diouane, S. Gratton, and L. N. Vicente, Globally convergent evolution strategies for constrained optimization, Comput. Optim. Appl, vol.62, issue.2, pp.323-346, 2015.
DOI : 10.1007/s10589-015-9747-3
URL : https://hal.archives-ouvertes.fr/hal-01391788

S. Wessing and M. Preuss, The true destination of EGO is multi-local optimization, 20017 IEEE Latin American Conference on Computational Intelligence, LA-CCI, pp.1-6, 2017.

S. and L. Digabel, Algorithm 909: NOMAD: nonlinear optimization with the MADS algorithm, ACM Trans. Math. Softw, vol.37, issue.4, p.44, 2011.

T. D. Plantenga, Hopspack 2.0 user manual, pp.2009-6265
DOI : 10.2172/1000278
URL : https://digital.library.unt.edu/ark:/67531/metadc844978/m2/1/high_res_d/1000278.pdf

G. Fasano, G. Liuzzi, S. Lucidi, and F. Rinaldi, A linesearch-based derivative-free approach for nonsmooth constrained optimization, SIAM J. Optim, vol.24, issue.3, pp.959-992, 2014.
DOI : 10.1137/130940037
URL : https://iris.unive.it/bitstream/10278/42404/1/94003-gg.pdf

J. Mo?kus, On Bayesian methods for seeking the extremum, Optimization Techniques IFIP Technical Conference, pp.400-404, 1975.

D. R. Jones, M. Schonlau, and W. J. Welch, Efficient global optimization of expensive black-box functions, J. Glob. Optim, vol.13, issue.4, pp.455-492, 1998.

A. Forrester, A. Sobester, and A. Keane, Engineering Design via Surrogate Modelling: A Practical Guide, 2008.

Z. Wang and S. Jegelka, Max-value entropy search for efficient Bayesian optimization, 34th International Conference on Machine Learning, vol.70, pp.3627-3635, 2017.

M. A. Gelbart, Constrained Bayesian Optimization and Applications, 2015.

V. Picheny, A stepwise uncertainty reduction approach to constrained global optimization, pp.787-795, 2014.

V. Picheny, R. B. Gramacy, S. Wild, and S. L. Digabel, Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian, Advances in Neural Information Processing Systems, pp.1435-1443, 2016.

S. Shan and G. G. Wang, Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions, Struct. Multidiscip. Optim, vol.41, issue.2, pp.219-241, 2010.

A. I. Forrester and A. J. Keane, Recent advances in surrogate-based optimization, Prog. Aerosp. Sci, vol.45, issue.1, pp.50-79, 2009.
DOI : 10.1016/j.paerosci.2008.11.001
URL : https://eprints.soton.ac.uk/65935/1/Forr_09.pdf

G. G. Wang and S. Shan, Review of metamodeling techniques in support of engineering design optimization, J. Mech. Des, vol.129, issue.4, pp.370-380, 2007.

N. V. Queipo, R. T. Haftka, W. Shyy, T. Goel, R. Vaidyanathan et al., Surrogate-based analysis and optimization, Prog. Aerosp. Sci, vol.41, issue.1, pp.1-28, 2005.

C. E. Rasmussen and C. K. Williams, Gaussian Processes for Machine Learning, vol.1, 2006.

G. Matheron, Principles of geostatistics, Econ. Geol, vol.58, issue.8, pp.1246-1266, 1963.
DOI : 10.2113/gsecongeo.58.8.1246

J. Sacks, W. J. Welch, T. J. Mitchell, and H. P. Wynn, Design and analysis of computer experiments, Stat. Sci, pp.409-423, 1989.

J. P. Kleijnen, Kriging metamodeling in simulation: a review, Eur. J. Oper. Res, vol.192, issue.3, pp.707-716, 2009.
DOI : 10.2139/ssrn.980063
URL : https://pure.uvt.nl/ws/files/818397/dp2007-13.pdf

M. J. Sasena, P. Y. Papalambros, and P. Goovaerts, The use of surrogate modeling algorithms to exploit disparities in function computation time within simulationbased optimization, The Fourth World Congress of Structural and Multidisciplinary Optimization, pp.1-6, 2001.

F. Palacios, J. J. Alonso, M. Colonno, J. Hicken, and T. Lukaczyk, Adjoint-based method for supersonic aircraft design using equivalent area distributions, AIAA Pap, vol.269, p.2012, 2012.
DOI : 10.2514/6.2012-269

Z. Han, R. Zimmermann, and S. Görtz, A New Cokriging Method for VariableFidelity Surrogate Modeling of Aerodynamic Data, p.2010, 2010.

T. Benamara, P. Breitkopf, I. Lepot, and C. Sainvitu, Adaptive infill sampling criterion for multi-fidelity optimization based on Gappy-POD, Struct. Multidiscip. Optim, vol.54, issue.4, pp.843-855, 2016.
DOI : 10.1007/s00158-016-1440-3
URL : https://hal.archives-ouvertes.fr/hal-01993098

J. Liu, W. Song, Z. Han, and Y. Zhang, Efficient aerodynamic shape optimization of transonic wings using a parallel infilling strategy and surrogate models, Struct. Multidiscip. Optim, vol.55, issue.3, pp.925-943, 2017.

Y. Ma, W. Zhou, and Q. Han, Research of multi-point infill criteria based on multiobjective optimization front and its application on aerodynamic shape optimization, Adv. Mech. Eng, vol.9, issue.6, p.1687814017703340, 2017.

M. A. Bouhlel, N. Bartoli, A. Otsmane, and J. Morlier, Improving kriging surrogates of high-dimensional design models by Partial Least Squares dimension reduction, Struct. Multidiscip. Optim, issue.5, pp.935-952, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01598259

M. A. Bouhlel, N. Bartoli, A. Otsmane, and J. Morlier, An improved approach for estimating the hyperparameters of the Kriging model for high-dimensional problems through the partial least squares method, Math. Probl. Eng, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01840458

M. A. Bouhlel, N. Bartoli, R. G. Regis, A. Otsmane, and J. Morlier, Efficient global optimization for high-dimensional constrained problems by using the Kriging models combined with the partial least squares method, Eng. Optim, pp.1-16, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01717251

T. Hastie, R. Tibshirani, J. Friedman, and J. Franklin, The elements of statistical learning: data mining, inference and prediction, Math. Intell, vol.27, issue.2, pp.83-85, 2005.

D. Bettebghor, N. Bartoli, S. Grihon, J. Morlier, and M. Samuelides, Surrogate modeling approximation using a mixture of experts based on EM joint estimation, Struct. Multidiscip. Optim, issue.2, pp.243-259, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01852300

Y. Tenne, An optimization algorithm employing multiple metamodels and optimizers, Int. J. Autom. Comput, vol.10, issue.3, pp.227-241, 2013.

M. Sasena, P. Papalambros, and P. Goovaerts, Global optimization of problems with disconnected feasible regions via surrogate modeling, 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, 2002.

S. Chen, Z. Jiang, S. Yang, and W. Chen, Multimodel fusion based sequential optimization, AIAA J, vol.55, issue.1, pp.241-254, 2016.

N. Bartoli, M. Bouhlel, I. Kurek, R. Lafage, T. Lefebvre et al., Improvement of efficient global optimization with application to aircraft wing design, 17th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, p.4001, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02047975

, AIAA Aerodynamic Design Optimization Discussion Group, 2019.

D. G. Krige, A Statistical Approach to Some Mine Evaluations and Allied Problems at the Witwatersrand, Master's thesis, 1951.

M. J. Sasena, P. Papalambros, and P. Goovaerts, Exploration of metamodeling sampling criteria for constrained global optimization, Eng. Optim, vol.34, issue.3, pp.263-278, 2002.

M. Schonlau, W. J. Welch, and D. R. Jones, Global Versus Local Search in Constrained Optimization of Computer Models, Lecture Notes-Monograph Series, pp.11-25, 1998.

C. Audet, J. Denni, D. Moore, A. Booker, and P. Frank, A surrogate-model-based method for constrained optimization, 8th Symposium on Multidisciplinary Analysis and Optimization, p.4891, 2000.

J. M. Hernández-lobato, M. A. Gelbart, R. P. Adams, M. W. Hoffman, and Z. Ghahramani, A general framework for constrained Bayesian optimization using information-based search, J. Mach. Learn. Res, vol.17, issue.160, pp.1-53, 2016.

R. Priem, N. Bartoli, Y. Diouane, and S. Dubreuil, An adaptive feasibility approach for constrained bayesian optimization with application in aircraft design, Engopt: 6th International Conference on Engineering Optimization, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01959436

A. G. Watson and R. J. Barnes, Infill sampling criteria to locate extremes, Math. Geol, vol.27, issue.5, pp.589-608, 1995.

M. Sasena, Flexibility and Efficiency Enhancements for Constrained Global Design Optimization with Kriging Approximations, 2002.

D. Bettebghor and N. Bartoli, Approximation of the critical buckling factor for composite panels, Struct. Multidiscip. Optim, vol.46, issue.4, pp.561-584, 2012.

R. P. Liem, C. A. Mader, and J. R. Martins, Surrogate models and mixtures of experts in aerodynamic performance prediction for mission analysis, Aerosp. Sci. Technol, vol.43, pp.126-151, 2015.

M. A. Bouhlel, J. T. Hwang, N. Bartoli, R. Lafage, J. Morlier et al., A Python surrogate modeling framework with derivatives, Adv. Eng. Softw

P. S. Bradley, U. M. Fayyad, and C. Reina, Scaling clustering algorithms to large databases, pp.9-15, 1998.
DOI : 10.1145/545151.545176

M. F. Anjos and D. R. Jones, MOPTA 2008 benchmark, 2009.

J. S. Gray, J. T. Hwang, J. R. Martins, K. T. Moore, and B. A. Naylor, OpenMDAO: an open-source framework for multidisciplinary design, in: analysis, and optimization, Struct. Multidiscipl. Optim, 2019.
DOI : 10.1007/s00158-019-02211-z
URL : https://link.springer.com/content/pdf/10.1007%2Fs00158-019-02211-z.pdf

J. Parr, A. Keane, A. I. Forrester, and C. Holden, Infill sampling criteria for surrogatebased optimization with constraint handling, Eng. Optim, vol.44, issue.10, pp.1147-1166, 2012.
DOI : 10.1080/0305215x.2011.637556

M. J. Powell, A direct search optimization method that models the objective and constraint functions by linear interpolation, Advances in Optimization and Numerical Analysis, pp.51-67, 1994.
DOI : 10.1007/978-94-015-8330-5_4

C. Audet, S. L. Digabel, C. Tribes, and . User-guide, Les cahiers du GERAD, 2009.

J. Nocedal and S. J. Wright, Numerical Optimization, 2006.

R. B. Gramacy, G. A. Gray, S. L. Digabel, H. K. Lee, P. Ranjan et al., Modeling an augmented Lagrangian for blackbox constrained optimization, Technometrics, vol.58, pp.1-11, 2016.
DOI : 10.1080/00401706.2015.1014065
URL : http://arxiv.org/pdf/1403.4890

R. Jin, W. Chen, and A. Sudjianto, An efficient algorithm for constructing optimal design of computer experiments, J. Stat. Plan. Inference, vol.134, issue.1, pp.268-287, 2005.
DOI : 10.1115/detc2003/dac-48760

E. Jones, T. Oliphant, and P. Peterson, SciPy: open source scientific tools for Python, 2001.

M. Abramson, C. Audet, G. Couture, J. Dennis, S. L. Digabel et al., The NOMAD project, 2009.

V. Picheny, D. Ginsbourger, and O. Roustant, DiceOptim: Kriging-based optimization for computer experiments, 2014.

D. Kraft, A Software Package for Sequential Quadratic Programming, 1988.

C. Audet and J. E. Dennis, A progressive barrier for derivative-free nonlinear programming, SIAM J. Optim, vol.20, issue.1, pp.445-472, 2009.
DOI : 10.1137/070692662

G. K. Kenway, N. Secco, J. R. Martins, A. Mishra, and K. Duraisamy, An efficient parallel overset method for aerodynamic shape optimization, Proceedings of the 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 2017.
DOI : 10.2514/6.2017-0357
URL : https://deepblue.lib.umich.edu/bitstream/2027.42/143038/1/6.2017-0357.pdf

Z. Lyu, G. K. Kenway, C. Paige, and J. R. Martins, Automatic differentiation adjoint of the Reynolds-averaged Navier-Stokes equations with a turbulence model, 21st AIAA Computational Fluid Dynamics Conference, 2013.

P. Spalart and S. Allmaras, A one-equation turbulence model for aerodynamic flows, 30th Aerospace Sciences Meeting and Exhibit, Aerospace Sciences Meetings, 1992.

G. K. Kenway, G. J. Kennedy, and J. R. Martins, A CAD-free approach to highfidelity aerostructural optimization, Proceedings of the 13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference, 2010.
DOI : 10.2514/6.2010-9231
URL : https://deepblue.lib.umich.edu/bitstream/2027.42/83549/1/AIAA-2010-9231-250.pdf

E. Luke, E. Collins, and E. Blades, A fast mesh deformation method using explicit interpolation, J. Comput. Phys, vol.231, issue.2, pp.586-601, 2012.

R. E. Perez, P. W. Jansen, and J. R. Martins, pyOpt: a python-based object-oriented framework for nonlinear constrained optimization, Struct. Multidiscipl. Optim, vol.45, issue.1, pp.101-118, 2012.

G. K. Kenway and J. R. Martins, pyOptSparse -PYthon OPTimization (sparse) framework, 2018.

P. E. Gill, W. Murray, and M. A. Saunders, SNOPT: an SQP algorithm for large-scale constrained optimization, SIAM Rev, vol.47, issue.1, pp.99-131, 2005.

G. K. Kenway and J. R. Martins, Buffet onset constraint formulation for aerodynamic shape optimization, AIAA J, vol.55, issue.6, pp.1930-1947, 2017.

R. P. Liem, J. R. Martins, and G. K. Kenway, Expected drag minimization for aerodynamic design optimization based on aircraft operational data, Aerosp. Sci. Technol, vol.63, pp.344-362, 2017.

S. Chen, Z. Lyu, G. K. Kenway, and J. R. Martins, Aerodynamic shape optimization of the common research model wing-body-tail configuration, J. Aircr, vol.53, issue.1, pp.276-293, 2016.

G. K. Kenway and J. R. Martins, Multipoint aerodynamic shape optimization investigations of the common research model wing, AIAA J, vol.54, issue.1, pp.113-128, 2016.

G. K. Kenway, G. J. Kennedy, and J. R. Martins, Scalable parallel approach for high-fidelity steady-state aeroelastic analysis and derivative computations, AIAA J, vol.52, issue.5, pp.935-951, 2014.

T. R. Brooks, G. K. Kenway, and J. R. Martins, Benchmark aerostructural models for the study of transonic aircraft wings, AIAA J, vol.56, issue.7, pp.2840-2855, 2018.

D. A. Burdette and J. R. Martins, Design of a transonic wing with an adaptive morphing trailing edge via aerostructural optimization, Aerosp. Sci. Technol, vol.81, pp.192-203, 2018.