A hybrid meta-model based global optimization method for expensive problems. (October 2019) - Gu, Jichao; Li, Wenqi; Cai, Yongzhou
Highlights: Multiple meta-models used together can increase the robustness of the method. The important region strategy can greatly increase the performance. The search both in the important and remaining region can avoid the local optimum. The search in the whole design space can further demonstrate the global optimum. Multiple screening strategy can avoid the poor candidate points. Abstract: The meta-model based global optimization algorithms usually select the new promising points from a large set of points, which are generated using the Latin hypercube design (LHD) and evaluated by the meta-model. Once the poor points are generated by the random number based LHD, the desired results may not be obtained. In this work, a hybrid meta-model based global optimization method (HMGO) is proposed. In this method, three different meta-model, kriging, radial basis functions (RBF) and quadratic function (QF) are used together in the search process. And multiple sets of large points are generated and multiple screening strategy is used for the selection of the new promising points to avoid the poor points. Through test by six benchmark math function with the number of the variables ranging from 10 to 24 and compared with the famous efficient global optimization (EGO), the proposed method shows excellent accuracy, efficiency and robustness. The HMGO method is then applied in a vehicle lightweight design problem with 30 design variables, desired results have been obtained.