Hybrid meta-model-based design space exploration method for expensive problems

Multiple meta-models used together in the search process at least can offer an insurance against the poorly fitted meta-models and can improve robustness of the predictions, compared with the single meta-model based methods. In this work, a hybrid meta-model-based design space exploration (HMDSE) method is proposed. In the proposed method, a part of the current expensive points which are evaluated by the expensive problems to be solved are used firstly to construct a so-called important region. And then, three representative meta-models, kriging, radial basis functions (RBF), and quadratic function (QF), are used in the search of the obtained important region. To avoid the local minima, the remaining region will be searched simultaneously. In addition, the whole design space will also be searched to further demonstrate the global optimum. Through test by six benchmark math functions with design variables ranging from 10 to 24, the proposed HMDSE method shows great accuracy, efficiency, and robustness compared with the efficient global optimization (EGO). Then, it is applied in a practical vehicle lightweight design problem with 30 design variables, achieving desired results.