A SURROGATE-BASED OPTIMIZATION DESIGN METHOD BASED ON HYBRID INFILL SAMPLING CRITERION

In engineering optimization design, numerical simulation calculation of structural response is expensive and time-consuming, which brings great challenges to compute-intensive optimization design. Therefore, the surrogate-based sequential optimization method has been well studied and widely used. Firstly, the framework of the surrogate-based sequence optimization is summarized at first. Secondly, a model-independent hybrid infill sampling criterion is developed in view of the insufficiency in existing methods. The new sample points generated during optimization process are distributed in the neighborhood of the current minimum value and the region with largest cross-validation error in the design space. The local exploitation and global exploration can be carried out simultaneously to find accurate global optimal solution. Thirdly, hybrid infill sampling criterion is introduced into the surrogate-based optimization framework combined with particle swarm optimization algorithm, and an efficient surrogate-based optimization design method is proposed. Finally, the proposed method is verified by mathematical and engineering examples. Compared with the optimization method by the grounds of traditional criterions, the proposed method can keep the tradeoff of accuracy and efficiency, which has better global optimization characteristics.