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Optimisation multi-objectifs à base de métamodèle pour des applications en mise en forme des métaux

Published online by Cambridge University Press:  20 December 2010

Mohsen Ejday  and
Lionel Fourment
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Abstract

Pour appliquer les algorithmes d’optimisation multi-objectifs à des problèmes de mise en forme des métaux très coûteux en temps de calcul, nous étudions le couplage de l’algorithme génétique NSGA-II proposé par Deb à un métamodèle inspiré de la méthode des différences finies sans maillage de Liszka et Orkisz. Nous soulignons l’importance d’améliorer itérativement le métamodèle au cours des itérations d’optimisation, et la possibilité de déterminer avec précision des fronts optimaux de Pareto des problèmes multi-objectifs étudiés en moins d’une centaine de calculs.

Keywords

Optimisation multi-objectifsdifférences finis sans maillageNSGA-IImétamodèlemise en forme des métauxMulti-objective optimizationmeshless finite difference methodNSGA-IImetal forming
Type
Research Article
Information
Mechanics & Industry , Volume 11 , Issue 3-4: Giens 2009 , May 2010 , pp. 223 - 233
Copyright
© AFM, EDP Sciences 2010

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References

Références

K. Deb, Multi-Objective Optimisation using Evolutionary Algorithms, John Wiley & Sons, Chichester, 2001
Emmerich, M., Beume, N., Naujoks, B., An EMO algorithm using the hyper volume measure as selection criterion, Evolutionary Multi-Criterion Optimization 3410 (2005) 62–76 CrossRefGoogle Scholar
Beume, N., Naujoks, B., Emmerich, M., SMS-EMOA : Multi-objective selection based on dominated hypervolume, Eur. J. Res. 181 (2007) 1653–1669 Google Scholar
Krok, J., An Extended Approach to Error Control in Experimental and Numerical Data Smoothing and Evaluation Using the Meshless FDM, Meshfree Computational Mechanics 11 (2002) 913–945 Google Scholar
K. Pawan, S. Nain, K. Deb, A Multi-Objective Optimization Procedure with Successive Approximate Models, KanGAL Report Number 2005002
K. Pawan, S. Nain, K. Deb, A Multi-Objective Search and Optimization Procedure with Successive Approximate Models, KanGAL Report Number 2004012
Goal, T., Vaidyanathan, R., Haftka, R.T., Shyy, W., Queipo, N.V., Tucker, K., Response surface approximation of Pareto optimal front in multi-objective optimization, Comput. Methods Appl. Mech. Eng. 196 (2007) 879–893 CrossRefGoogle Scholar
K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A Fast and Elitist Multi-Objective Genetic Algorithm : NSGA-II, KanGAL Report Number 200001
M. Emmerich, B. Naujoks, Metamodel-Assisted Multiobjective Optimization with Implicit Constraints and its Application in Airfoil Design, International Conference and Advanced Course ERCOFTAC, Athens, Greece, 2004
Ingarao, G., Lorenzo, R.D., F. Micari. Internal pressure and counter-punch action design in Y-shaped tube hydroforming processes : A multi-objective optimisation approach, Comput. Struct. 87 (2009) 591–602 CrossRefGoogle Scholar
M. Emmerich, A. Giotis, M. Özdemir, T. Bäck, K. Giannakoglou, Metamodel-assisted evolution strategies,International Conference on parallel problem solving from nature Springer, Berlin, Germany, 2002
Emmerich, M., Giannakoglou, K., Naujoks, B., Single- and Multiobjective Evolutionary Optimization Assisted by Gaussian Random Field Metamodels, IEEE Trans. Evol. Comput. 10 (2006) 421–439 CrossRefGoogle Scholar
M. Emmerich, B. Naujoks, Metamodel-Assisted Multiobjective Optimization Algorithms and their application in Airfoil Design, in Proc. 5th Int. Conf. Adaptive Computing in Design and Manufacture VI (ACDM), Springer, London, 2004, pp. 249–260
M.H.A Bonte, L. Fourment, T.T. Do, A.H. Van den Boogaard, J. Huétink. Optimisation of metal forming processes using Finite Element simulations : A sequential approximate optimization algorithm and its comparision to other algorithms by application to forging, Structural and Multidisciplinary optimization
Liszka, T., Orkisz, J., The finite difference method at arbitrary irregular grids and its application in applied mechanics, Comput. Struct. 11 (1980) 83–95 CrossRefGoogle Scholar
M.T.T. Do, Optimisation de forme en forgeage 3D, Ph.D. thesis, Mines ParisTech.
Breitkopf, P., Naceur, H., Rassineux, A., P. Villon. Moving Least squares response surface approximation : Formulation and metal forming applications, Comput. Struct. 83 (2005) 1411–1428 CrossRefGoogle Scholar
K. Deb, L. Thiele, M. Laumanns, E. Zitzler, Scalable Test Problems for Evolutionary Multi-Objective Optimization, in A. Abraham, L. Jain, R. Goldberg (éds.), Evolutionary Multiobjective Optimization. Theoretical Advances and Applications, Springer, USA, 2005, pp. 105–145
K. Deb, L. Thiele, M. Laumanns, E. Zitzler, Scalable Multi-Objective Optimization Test Problems, in Congress on Evolutionary Computation (CEC) Piscataway, New Jersey, May 2002, Vol. 1, pp. 825–830
Deb, K., Multi-objective genetic algorithms: Problem difficulties and construction of test problems, Evol. Comput. J. 7 (1999) 205–230 CrossRefGoogle ScholarPubMed