A Sequential Optimization Method 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 sequential approximation optimization method is developed. Multi-point sampling criterion is used to select the new samples by considering the distributions of the initial samples and the characteristics of the predicted target function. The proposed method selects more than one new sample for each optimization iteration, thus it can be performed by parallel computation or multi-computer runs which improve effectively the computational efficiency. Take tow typical mathematical functions as examples, the proposed method is compared with expected improvement criterion method and the results show the proposed method can effectively search the global optimum.