A High-Precision Surrogate Modeling Method Based on Parallel Multipoint Expected Improvement Point Infill Criteria for Complex Simulation Problems

In order to overcome the problem of the low fitting accuracy of the expected improvement point infill criteria (EI) and the improved expected improvement point infill criteria (IEI), a high-precision surrogate modeling method based on the parallel multipoint expected improvement point infill criteria (PMEI) is presented in this paper for solving large-scale complex simulation problems. The PMEI criterion takes full advantage of the strong global search ability of the EI criterion and the local search ability of the IEI criterion to improve the overall accuracy of the fitting function. In the paper, the detailed steps of the PMEI method are introduced firstly, which can add multiple sample points in a single iteration. At the same time, in the process of constructing the surrogate model, it is effective to avoid the problem of the low fitting accuracy caused by adding only one new sample point in each iteration of the EI and IEI criteria. The numerical examples of the classical one-dimensional function and two-dimensional function clearly demonstrate the accuracy of the fitting function of the proposed method. Moreover, the accuracy of the multi-objective optimization surrogate model of a truck cab constructed by the PMEI method is tested, which proves the feasibility and effectiveness of the proposed method in solving high-dimensional modeling problems. All these results confirm that the Kriging model developed by the PMEI method has high accuracy for low-dimensional problems or high-dimensional complex problems.