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.

在许多工程设计领域中，克里格模型已被广泛用于近似昂贵的黑盒问题。如何选择合适的采样策略以产生新的昂贵的更新点至关重要。为此，提出了一种具有双重采样标准的自适应克里格方法（AKM-DSC）。在每次迭代过程中，分别通过信任区域（TR）策略优化基于Kriging模型的最大曲率准则和最大方差准则，以产生两个候选点。然后，使用一种新的筛选方法从两个候选对象中确定最终的昂贵评估点。将该方法与两种典型的Kriging建模方法进行了比较。十七个基准函数的比较结果验证了所提方法能够生成较高精度的克里格模型。最后，