Offline data‐driven evolutionary optimization based on model selection

In data-driven evolutionary optimization, since different models are suitable for different types of problems, an appropriate surrogate model to approximate the real objective function is of great significance, especially in offline optimization. In this paper, an offline data-driven evolutionary optimization framework based on model selection (MS-DDEO) is proposed. A model pool is constructed by four radial basis function models with different smoothness degrees for model selection. Meanwhile, two model selection criteria are designed for offline optimization. Among them, Model Error Criterion uses some ranking-top data as test set to test the ability to predict optimum. Distance Deviation Criterion estimate reliability by distances between predicted solution and some ranking-top data. Combining the two criteria, we select the most suitable surrogate model for offline optimization. Experiments show that this method can effectively select suitable models for most test problems. Results on the benchmark problems and airfoil design example show that the proposed algorithm is able to handle offline problems with better optimization performance and less computational cost than other state-of-the-art offline data-driven optimization algorithms.