Sampling method with multi-point sampling algorithm based on vertical distance in Kriging model

Since the surrogate model can reduce disciplinary analysis time effectively, it is widely used in optimization. The accuracy and the calculation of surrogate model depend on the sampling points in the design space. In order to establish approximation model fitting the data well, an adaptive sampling algorithm based on Kriging model is put forward. The algorithm is based on vertical distance and integrated mean square error (IMSE) criterion to ensure prediction accuracy while reducing the number of samples. The vertical distance is adopted as the standard to decide the design variables and Gauss function as correlation function for design points Then the sampling region of experiment design is updated near the edge of the contour. Taking a practical example, the proposed algorithm is compared with the multi-point sampling criterion, the results show that the agent model built by the proposed method can effectively search both the local and global optimum using less sampling points.