Research on an Accuracy Optimization Algorithm of Kriging Model Based on a Multipoint Filling Criterion

Shande Li, Shuai Yuan, Shaowei Liu, Jian Wen and Qibai Huang
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Shande Li: State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Shuai Yuan: State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Shaowei Liu: State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Jian Wen: Hubei Innovation Institute of Mobile Emergency Equipment Manufacturing, Hubei Institute of Specialty Vehicle, Suizhou 441300, China
Qibai Huang: State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Mathematics, 2022, vol. 10, issue 9, 1-11

Abstract: The optimization method based on the surrogate model has been widely used in the simulation and calculation process of complex engineering models. However, in this process, the low accuracy and computational efficiency of the surrogate model has always been an urgent problem that needs to be solved. Aimed at this problem, combined with the two characteristics of global search and local detection, a filling criterion with multiple points is firstly proposed named maximum of expected improvement & minimizing the predicted objective function & maximum of root mean squared error (EI&MP&RMSE) in this paper. Furthermore, the optimization procedure of the surrogate model based on EI&MP&RMSE is concluded. Meanwhile, the classical one-dimensional and two-dimensional functions are applied to verify the accuracy of the proposed method. The difference in the accuracy and mean square error of the surrogate model under different infill points criteria are analyzed. As expected, it shows that this method can effectively improve the accuracy of the surrogate model and reduce the number of iterations. It has extensive practicability and serviceability for the optimization of complex engineering structures.

Keywords: Kriging model; filling criterion; EI&MP&RMSE (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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