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.
Published Version
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