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Critères de robustesse pour l'optimisation sur métamodèle de krigeage

Abstract : In the context of robust shape optimization, the estimation cost of some physical models is reduced by the use of a response surface. The multi objective methodology for robust optimization that requires the partitioning of the Pareto front (minimization of the function and the robustness criterion) has already been developed. However, the efficient estimation of the robustness criterion in the context of time-consuming simulation has not been much explored. Since the function, the first and second derivatives are given by the majority of industrial codes, we propose a robust optimization procedure based on the prediction of the function and its derivatives by a kriging. The usual moment $2$ is replaced by an approximated version using Taylor theorem. A Pareto front of the robust solutions is generated by a genetic algorithm named NSGA-II. This algorithm gives a Pareto front in an reasonable time of calculation. Our method is illustrated on a simulated test case in dimension 2.
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Contributor : Mélina Ribaud <>
Submitted on : Tuesday, September 22, 2020 - 2:27:06 PM
Last modification on : Tuesday, March 30, 2021 - 12:08:03 PM
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  • HAL Id : hal-02945584, version 1


Christophette Blanchet-Scalliet, Frédéric Gillot, Céline Helbert, Mélina Ribaud. Critères de robustesse pour l'optimisation sur métamodèle de krigeage. JDS 2018 (SFDS), May 2018, Paris Saclay, France. ⟨hal-02945584⟩



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