Sequential sampling designs based on space reduction

Abstract

In the field of engineering design and optimization, metamodels are widely used to replace expensive simulation models in order to reduce computing costs. To improve the accuracy of metamodels effectively and efficiently, sequential sampling designs have been developed. In this article, a sequential sampling design using the Monte Carlo method and space reduction strategy (MCSR) is implemented and discussed in detail. The space reduction strategy not only maintains good sampling properties but also improves the efficiency of the sampling process. Furthermore, a local boundary search (LBS) algorithm is proposed to efficiently improve the performance of MCSR, which is called LBS-MCSR. Comparative results with several sequential sampling approaches from low to high dimensions indicate that the space reduction strategy generates samples with better sampling properties (and thus better metamodel accuracy) in less computing time.

Keywords:

• space reduction
• sequential sampling designs
• LBS
• metamodel accuracy

Funding

The authors appreciate the financial support from the PhD Programs Foundation of Liaoning Province [20131019], the Fundamental Research Funds for the Central Universities [DUT14QY36], the National Basic Research Program of China [2009CB724303] and the National Natural Science Foundation of China [51308090].