A SEQUENTIAL OPTIMIZATION METHOD WITH MULTI-POINT SAMPLING CRITERION BASED ON KRIGING SURROGATE MODEL

A multi-point sampling criterion considering the predictor and its uncertainty simultaneously is proposed based on Kriging surrogate model, and a kind of sequential approximation optimization method is developed. Multi-point sampling criterion is used to add the new samples by considering the distribution of the initial samples and the characteristics of the predicted objective function. The proposed method selects more than one new sample point for each optimization iteration, thus it can be performed by parallel computation or multi-computer runs which improves the computational efficiency distinctly. Take tow typical mathematical functions as examples, the proposed method is compared with expected improvement criterion and the results show the proposed method can effectively search the global optimum. The proposed optimization method is used to optimize injection molding process for a box-shape part, and the optimization result shows the method is effective for the reduction of warpage.