An adaptive Kriging method with double sampling criteria applied to hydrogen preparation case. (13th November 2020) - Li, Yaohui
Abstract: Kriging model has been widely used to approximate expensive black-box problems in many engineering design fields. How to choose an appropriate sampling strategy to produce new expensive updated points is crucial. For this purpose, an adaptive Kriging method with double sampling criteria (AKM-DSC) is proposed. During every iteration of it, maximum curvature criterion and maximum variance criterion based on Kriging model are respectively optimized by trust region (TR) strategy to produce two candidate points. And then, a new screening method is used to determine final expensive-evaluation points from the two candidates. The proposed method is compared with the two typical Kriging modeling methods. The comparison results of seventeen benchmark functions verify that the proposed method can generate higher accuracy Kriging model. Finally, a hydrogen preparation case illustrates the engineering application value of the AKM-DSC method. Highlights: An adaptive Kriging method with double sampling criteria is proposed. Two infill sampling criteria are used to improve approximate accuracy of model. The comparison results with other algorithms reveal the superiority of the method. The hydrogen preparation case is verified.