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

Author

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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
    Hubei Innovation Institute of Mobile Emergency Equipment Manufacturing, Hubei Institute of Specialty Vehicle, Suizhou 441300, 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
    Hubei Innovation Institute of Mobile Emergency Equipment Manufacturing, Hubei Institute of Specialty Vehicle, Suizhou 441300, China)

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.

Suggested Citation

  • Shande Li & Shuai Yuan & Shaowei Liu & Jian Wen & Qibai Huang, 2022. "Research on an Accuracy Optimization Algorithm of Kriging Model Based on a Multipoint Filling Criterion," Mathematics, MDPI, vol. 10(9), pages 1-11, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1548-:d:808572
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    References listed on IDEAS

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    1. D. Huang & T. Allen & W. Notz & N. Zeng, 2006. "Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models," Journal of Global Optimization, Springer, vol. 34(3), pages 441-466, March.
    2. Pham, Quang Hung & Gagnon, Martin & Antoni, Jérôme & Tahan, Antoine & Monette, Christine, 2022. "Prediction of hydroelectric turbine runner strain signal via cyclostationary decomposition and kriging interpolation," Renewable Energy, Elsevier, vol. 182(C), pages 998-1011.
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