An Improved Surrogate Based Optimization Method for Expensive Black-box Problems

For expensive black-box problems, surrogate modelling techniques are generally used to decrease the computational source. In this study, an improved surrogate based optimization (SBO) method is presented to solve the real-world engineering applications with expensive black-box objective responses. An optimized ensemble of surrogates combing three typical surrogate modelling techniques is adapted to efficiently predict the objective response. Meanwhile, the hierarchical design space reduction (HSR) strategy is employed for obtaining the smaller design subspace for improving the optimization efficiency. During the search, all test problems are considered as the real-world engineering applications whereas the actual global optima as well as the function characteristics are unknown in advance. The results show that the proposed method is superior in identifying the global optimum.