Hybrid Meta-Model Based Design Space Differentiation Method for Expensive Problems

Abstract

In this work, a hybrid meta-model based design space differentiation (HMDSD) method is proposed for practical problems. In the proposed method, an iteratively reduced promising region is constructed using the expensive points, with two different search strategies respectively applied inside and outside the promising region. Besides, the hybrid meta-model strategy applied in the search process makes it possible to solve the complex practical problems. Tested upon a serial of benchmark math functions, the HMDSD method shows great efficiency and search accuracy. On top of that, a practical lightweight design demonstrates its superior performance.