Surrogate Assisted Large-Scale Expensive Optimization With Difference-Based Infill Criterion

Large-scale expensive optimization problems (LSEOPs) are characterized by a large number of dimensions in the decision space and expensive objective functions. The curse of dimensionality poses challenges to achieving the optimal solution, especially when limited objective evaluations are allowed. In this paper, the cooperative coevolution framework is used. A PSO variant is proposed to update solutions for each sub-problem. Then, a difference-based infill criterion is proposed to select a solution for real expensive objective evaluation. A number of experiments are conducted on CEC'2013 test problems to verify the performance of the proposed method. The experimental results show that our proposed method can obtain better performance than the four representative methods presented for solving LSEOPs.