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An efficient parallel global optimization strategy based on Kriging properties suitable for material parameters identification

Abstract : Material parameters identification by inverse analysis using finite element computations leads to the resolution of complex and time-consuming optimization problems. One way to deal with these complex problems is to use meta-models to limit the number of objective function computations. In this paper, the Efficient Global Optimization (EGO) algorithm is used. The EGO algorithm is applied to specific objective functions , which are representative of material parameters identification issues. Isotropic and anisotropic correlation functions are tested. For anisotropic correlation functions, it leads to a significant reduction of the computation time. Besides, they appear to be a good way to deal with the weak sensitivity of the parameters. In order to decrease the computation time, a parallel strategy is defined. It relies on a virtual enrichment of the meta-model, in order to compute q new objective functions in a parallel environment. Different methods of choosing the qnew objective functions are presented and compared. Speed-up tests show that Kriging Believer (KB) and minimum Constant Liar (CLmin) enrichments are suitable methods for this parallel EGO (EGO-p) algorithm. However, it must be noted that the most interesting speed-ups are observed for a small number of objective functions computed in parallel. Finally, the algorithm is successfully tested on a real parameters identification problem.
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Submitted on : Tuesday, May 26, 2020 - 11:00:12 AM
Last modification on : Wednesday, November 4, 2020 - 3:52:58 PM

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Émile Roux, Yannick Tillier, Salim Kraria, Pierre-Olivier Bouchard. An efficient parallel global optimization strategy based on Kriging properties suitable for material parameters identification. Archive of Mechanical Engineering, De Gruyter, In press, ⟨10.24425/ame.2020.131689⟩. ⟨hal-02624246⟩

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