Hybrid meta-model based search method for expensive problems.

The complexity and opaque characteristics of the practical expensive problems hinder the further applications of the single meta-model based optimization algorithms. In this work, a hybrid meta-model based search method (HMBSM) is presented. In this method, an important region is firstly constructed using a part of the expensive points which are evaluated by the expensive problems. Then, three meta-models with different fitting techniques are used together both in the important region and the remaining region. The whole design space will also be searched simultaneously to further avoid the local optima. Through intensive test by six benchmark math functions with the variables ranging from 10 to 24 and compared with the efficient global optimization (EGO), hybrid meta-model based design space management (HMDSM) method and multiple meta-model based design space differentiation (MDSD) method, the proposed HMBSM method shows excellent accuracy, efficiency and robustness. Then, the proposed method is applied in a vehicle lightweight design involving finite element analysis with 30 design variables, reducing 11.4 kg of weight.