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.

JO - Archive of Mechanical Engineering L1 - http://sd.czasopisma.pan.pl/Content/115021/PDF/AME_2020_131689.pdf L2 - http://sd.czasopisma.pan.pl/Content/115021 PY - 2020 IS - No 2 EP - 195 KW - global optimization KW - parallel computation KW - Kriging meta-model KW - inverse analysis A1 - Roux, Emile A1 - Tillier, Yannick A1 - Kraria, Salim A1 - Bouchard, Pierre-Olivier PB - Polish Academy of Sciences, Committee on Machine Building VL - vol. 67 JF - Archive of Mechanical Engineering DA - 2020.05.15 T1 - An efficient parallel global optimization strategy based on Kriging properties suitable for material parameters identification SP - 169 UR - http://sd.czasopisma.pan.pl/dlibra/docmetadata?id=115021 DOI - 10.24425/ame.2020.131689 ER -