T. Achard, Techniques de calcul de gradient aéro-structure haute-fidélité pour l'optimisation de voilure flexibles, 2017.

M. Allen and K. Maute, Reliability-based design optimization of aeroelastic structures. Structural and Multidisciplinary Optimization, vol.27, pp.228-242, 2004.

L. Andrieu, Optimisation sous contrainte en probabilité, 2004.

S. Au and J. L. Beck, Estimation of small probabilities in high dimension by subset simulation, Probabilistic Engineering Mechanics, vol.16, pp.263-277, 2001.

A. Auger, J. Bader, D. Brockhoff, and E. Zitzler, Theory of hypervolume indicator : Optimal µ-distributions and the choice of the reference point, Proceedings of the Tenth ACM SIGEVO Workshop Foundations of Genetic Algorithms, pp.87-102, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00430540

R. Bellman and L. Zadeh, Decision-making in fuzzy environment, Management Science, vol.17, pp.141-161, 1970.

J. Bonnans, J. Gilbert, C. Lemarechal, and C. Sagastizábal, Numerical Optimization Numerical Optimization, Theoretical and Practical Aspects, 2006.

H. Bonnel and J. Collonge, Stochastic optimization over a pareto set associated with stochastic multiobjective optimization problem, Journal of Optimization Theory and Applications, vol.162, issue.2, pp.405-427, 2014.

N. Bouleau, D. Lamberton, and B. Lapeyre, Une méthode numérique adaptée au calcul probabiliste des structures, Journal de Mécanique Théorique et Appliquée, vol.5, issue.5, 1986.

P. Breitkopf and R. F. Coelho, Multidisciplinary Design Optimization in Computational Mechanics, 2010.

J. V. Burke, A. S. Lewis, and M. L. Overton, Approximating subdifferentials by random sampling of gradients, Mathematics of Operations Research, vol.27, issue.3, pp.567-584, 2002.

. Bibliographie,

R. Caballero, E. Cerdá, . Del-mar-muñoz, and L. Rey, Stochastic approach versus multiobjective approach for obtaining efficient solutions in stochastic multiojective programming problems, European Journal of Operational Research, vol.158, pp.633-648, 2004.

F. Clarke, Optimization and Nonsmooth Analysis, Society for Industrial and Applied Mathematics, 1990.

R. F. Coelho and P. Breitkopf, Optimisation multidisciplinaire en mécanique 1 : démarche de conception, stratégie collaboratives et concourantes, multiniveaux de modèles et de paramètres, 2009.

Y. Collette and P. Siarry, Optimisation multiobjectif. Editions Eyrolles, 2011.

A. L. Custódio, F. A. Madeaira, A. I. Vaz, and L. N. Vincente, Direct multisearch for multiobjective optimization, SIAM Journal on Optimization, vol.21, issue.3, pp.1109-1140, 2011.

B. Dantzig, Linear programming under uncertainty, Management Science, vol.1, pp.197-206, 1955.

K. Deb, H. ;. Gupta, A. Hernández-aguirre, and E. Zitzler, Searching for robust pareto-optimal solutions in multi-objective optimization, Coello Coello, vol.3410, pp.150-164, 2005.

K. Deb, A. Pratap, S. Agarwal, and T. Et-meyarivan, A fast and elitist multiobjective genetic algorithm : Nsga-ii, IEEE Transactions on Evolutionary Computation, vol.6, issue.2, pp.182-197, 2002.

J. Désidéri, Multiple-Gradient Descent Algorithm (MGDA), 2009.

J. Désidéri, Quasi-riemannian multiple gradient descent algorithm for constrained multiobjective differential optimization, 2018.

J. Désidéri and R. Duvigneau, Parametric optimisation of pulsating jets in unsteady flow by multiple gradient descent algorithm (mgda), 2017.

J. Désidéri, R. Duvigneau, and A. Habbal, Multiobjective design optimization using nash games, éditeurs : Computational intelligence in Aerospace Science, Progress in Astronautics and Aeronautics, chapitre 16, 2014.

J. E. Diaz, J. Handl, and D. Xu, Evolutionary robust optimization in production planning interactions between number of objectives, sample size and choice of robustness measure, Computers & Operations Research, vol.79, pp.266-278, 2017.

J. Dodu, M. Goursat, A. Hertz, J. Quadrat, and M. Viot, Stochastic gradient methods for optimizing investments in electric power system studies, 1981.

E. D. Dolan and J. J. Et-moré, Benchmarking optimization software with performance profiles, Mathematical Programming, vol.91, issue.2, pp.201-213, 2002.

V. Dubourg, B. Sudret, and J. Bourinet, Reliability-based design optimization using kriging surrogates and subset simulation. Structural and Multidisciplinary Optimization, vol.44, pp.673-690, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00587311

R. Durrett, Probability Theory and Examples. Cambridge Series in Statistical and Probabilistic Mathematics, 2010.

A. Dvoretzky, On stochastic approximation, Proceeding of the Third Berkeley Symposium on Mathematical Statistics and Probability, vol.1, pp.39-55, 1956.

M. Ehrgott, Multicriteria optimization, 2006.

Y. Ermoliev, Stochastic quasigradient methods and their application to systems optimization, Stochastics, vol.9, pp.1-36, 1983.

Y. Ermoliev and R. Wets, Numerical Technics for Stochastic Optimization, 1988.

H. Federer, Geometric Measure Theory, 1996.

J. Fliege and R. Werner, Robust multiobjective optimization & applications in portfolio optimization, European Journal of Operational Research, vol.234, pp.422-433, 2014.

J. Fliege and H. Xu, Stochastic multiobjective optimization : Sample average approximations and applications, Journal of Optimization Theory and Applications, vol.151, issue.1, pp.135-162, 2011.

V. Gabrel, C. Murat, and A. Thiele, Recent advances in robust optimization : An overview, European Journal of Operational Research, vol.235, pp.471-483, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01495346

L. J. Gibson and M. F. Ashby, Cellular Solids : Structure and Properties, 1999.

S. Huband, P. Hingston, L. Barone, and L. While, A review of multiobjective test problems and a scalable problem toolkit, IEEE Transactions on Evolutionary Computation, vol.10, pp.477-506, 2006.

Y. Jin, M. Olhofer, and B. Sendhoff, Dynamic weighted aggregation for evolutionary multiobjective optimization : Why does it work and how ?, Proceedings of the Genetic and Evolutionary Computation Conference, pp.1042-1049, 2001.

R. Johnson and T. Zhang, Acclerating stochastic gradient descent using predective variance reduction, NIPS'13 Proceedings of the 26th International Conference on Neural Information Processing Systems, vol.1, pp.315-323, 2013.

J. Kang, M. Suh, and T. Lee, Robust economic optimization of process design under uncertainty. Engineering Optimization, vol.36, pp.51-75, 2004.

K. Klamroth, E. Kbis, A. Schbel, and C. Tammer, A unified approach to uncertain optimization, European Journal of Operational Research, vol.260, pp.805-819, 2017.

J. Knowles and D. Corne, The pareto evolution strategy : A new baseline algorithme for pareto multiobjective optimization, Piscataway, N., éditeur : Congres on Evolutionary Computation (CEC99), vol.1, pp.98-105, 1999.

J. Kovach and B. R. Cho, Development of a multidisciplinary multiresponse robust design optimization model. Engineering Optimization, vol.40, pp.805-819, 2008.

N. Le-roux, M. Schmidt, and F. Bach, A stochastic gradient method with exponential convergence rate for strongly-convex optimization with finite training sets, Advances in Neural Information Processing Systems, 2012.

P. Leite, Conception architecturale appliquée aux matériaux sandwichs pour propriétés multifonctionnelles, p.2, 2013.

N. Löhndorf, An empirical analysis of scenario generation methods for stochastic optimization, European Journal of Operational Research, vol.255, pp.121-132, 2016.

M. M. Mäkelä, V. Eronen, and N. Et-karmitsa, Optimization in Science and Engineering, chapitre On Nonsmooth Optimality Conditions with Generalized Convexities, pp.333-357, 2014.

M. M. Mäkelä, N. Karmitsa, and V. Et-eronen, On generalized pseudo and quasi-convexities for nonsmooth functions, vol.989, 2010.

M. M. Mäkelä, N. Karmitsa, and O. Wilppu, Multiobjective proximal bundle method for nonsmooth optimization. Rapport technique No 1120, 2014.

C. A. Mattson and A. Messac, Pareto frontier based concept selection under uncertainty, with visualization, Optimization and Engineering, vol.6, pp.85-115, 2005.

Q. Mercier, F. Poirion, and J. Désidéri, Smgda : an uncertainty based multiobjective optimization approach. illustration to an airplane composite material, Procedia Engineering, vol.199, pp.1199-1203, 2017.

Q. Mercier, F. Poirion, and J. Désidéri, Nonconvex multiobjective design optimization under uncertainty : a descent algorithm. application to sandwich plate design and reliability. Engineering Optimization, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01870135

Q. Mercier, F. Poirion, and J. Désidéri, A stochastic multiple gradient descent algorithm, European Journal of Operational Research, vol.271, issue.3, pp.808-817, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01833165

K. Miettinen, Nonlinear Multiobjective Optimization, 1999.

S. Missoum, C. Dribusch, and P. Beran, Reliability-based design optimization of nonlinear aeroelasticity problems, Journal of Aircraft, vol.47, issue.3, pp.992-998, 2010.

M. Moustapha, B. Sudret, and J. Bourinet, Quantile-based optimization under uncertainties using adaptative kriging surrogate models. Structural and Multidisciplinary Optimization, vol.54, pp.1403-1421, 2016.

A. Nemirovski and A. Shapiro, Scenario approximation of chance constraints, Probabilistic and Randomized Methods for Design under Uncertainty, 2006.

J. Neveu, Martingales à temps discret, 1972.

M. Nikbay, N. Fakkusoglu, and M. Kuru, Reliability-based aeroelastic optimization of a composite aircraft wing via fluid-structure interaction of high fidelity solvers, IOP Conf Series : Materials Science and Engineering, vol.10, 2010.

F. Poirion and Q. Mercier, Uncertainty-based multidisciplinary design optimization. a descent algorithm, Proceedings of the joint ICVRAM ISUMA UNCERTAINTIES conference, 2018.

F. Poirion, Q. Mercier, and J. Désidéri, Descent algorithm for nonsmooth stochastic multiobjective optimization, Computational Optimization and Applications, vol.68, issue.2, pp.317-331, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01660788

F. Poirion, Q. Mercier, and J. Désidéri, Descent methods for design optimization under uncertainty, Aerospacelab Journal, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01938525

T. Ray, K. Tai, and K. Chyeseow, Multiobjective design optimization by an evolutionary algorithm. Engineering Optimization, vol.33, pp.399-424, 2007.

N. Riquelme, C. Von-lücken, and B. Baran, Performance metrics in multiobjective optimization, Latin American Computing Conference (CLEI), 2015.

H. Robbins and S. Monro, A stochastic approximation method. The annals of mathematical statistics, pp.400-407, 1951.

R. Roy, S. Hinduja, and R. Teti, Recent advances in engineering design optimization : challenge and future trends, Manufactering Technology, vol.57, pp.697-715, 2008.

N. V. Sahinidis, Optimization under uncertainty : state-of-the-art and opportunities, Computers and Chemical Engineering, vol.28, issue.6-7, pp.971-983, 2004.

R. Schöbi and B. Sudret, Structural reliability analysis for p-boxes using multi-level meta-models, Probabilistic Engineering Mechanics, vol.48, pp.27-38, 2017.

S. Shalev-schwartz and T. Zhang, Stochastic dual coordinate ascent methods for regularized loss, The Journal of Machine Learning Research, vol.14, issue.1, pp.567-599, 2013.

A. Shapiro, Monte carlo sampling methods, Stochastic Programming, vol.10, pp.353-425, 2003.

G. Sun, G. Li, S. Zhou, H. Li, S. Hou et al., Crashworthiness design of vehicule by using multiobjective robust optimization. Structural and Multidisciplinary Optimization, vol.44, pp.99-110, 2011.

G. Sun, H. Zhang, J. Fang, G. Li, and Q. Liamis, Multi-objective and multi-case reliability-based design optimization for tailor rolled blank (trb) structures. Structural and Multidisciplinary Optimization, vol.55, pp.1899-1916, 2017.

Z. Wang, J. Guoa, M. Zheng, and Y. Wang, Uncertain multiobjective traveling salesman problem, European Journal of Operational Research, vol.241, pp.478-489, 2015.

A. Zerbinati, Algorithme à gradients multiples pour l'optimisation multiobjectif en simulation de haute fidélité : application à l'aérodynamique compressible. Theses, 2013.

E. Zitzler, K. Deb, and L. Thiele, Comparison of multiobjective evolutionary algorithms : Empirical results, Evolutionary Computation, vol.8, pp.173-195, 2000.

E. Zitzler and L. Thiele, Multiobjective evolutionary algorithms : a comparative case study and the strength pareto approach, Evolutionary Computation, vol.3, issue.4, pp.257-271, 1999.