A novel sequential exploration-exploitation sampling strategy for global metamodeling

Sampling strategy has direct impact on the accuracy of metamodel. In this paper, we propose a novel sequential exploration-exploitation sampling strategy for global metamodeling. A new criterion is proposed to focus on characteristic of output space. Space-filling requirement is treated as a constraint to avoid clustered sample points. The methodology developed in this paper is compared to existing methods using an analytic numerical case with two inputs. The results indicate that the proposed approach achieves the more desired accuracy of metamodel than the other approaches.