TY - JOUR
N2 - 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.
L1 - http://rhis.czasopisma.pan.pl/Content/115021/PDF/AME_2020_131689.pdf
L2 - http://rhis.czasopisma.pan.pl/Content/115021
PY - 2020
IS - No 2
EP - 169-195
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
SP - 169-195
T1 - An efficient parallel global optimization strategy based on Kriging properties suitable for material parameters identification
DA - 2020.05.15
UR - http://rhis.czasopisma.pan.pl/dlibra/publication/edition/115021
DOI - 10.24425/ame.2020.131689
ER -