Ensemble of surrogates with hybrid method using global and local measures for engineering design

Abstract

Surrogate models are usually used as a time-saving approach to reduce the computational burden of expensive computer simulations for engineering design. However, it is difficult to choose an appropriate model for an unknown design space. To tackle this problem, an effective method is forming an ensemble model that combines several surrogate models. Many efforts were made to determine the weight factors of ensemble, which include global and local measures. This article investigates the characteristics of global and local measures, and presents a new ensemble model which combines the advantages of these two measures. In the proposed method, the design space is divided into two parts, and different strategies are introduced to evaluate the weight factors in these two parts respectively. The results from numerical and engineering design cases show that the proposed ensemble model has satisfactory robustness and accuracy (it performs best for most cases tested in this article), while spending almost the equivalent modeling time (the additional cost is not more than 6.7% for any case tested in this article) compared with the combined global and local ensemble models.