Data-driven evolutionary sampling optimization for expensive problems

Surrogate models have shown to be effective in assisting evolutionary algorithms (EAs) for solving computationally expensive complex optimization problems. However, the effectiveness of the existing surrogate-assisted evolutionary algorithms still needs to be improved. A data-driven evolutionary sampling optimization (DESO) framework is proposed, where at each generation it randomly employs one of two evolutionary sampling strategies, surrogate screening and surrogate local search based on historical data, to effectively balance global and local search. In DESO, the radial basis function (RBF) is used as the surrogate model in the sampling strategy, and different degrees of the evolutionary process are used to sample candidate points. The sampled points by sampling strategies are evaluated, and then added into the database for the updating surrogate model and population in the next sampling. To get the insight of DESO, extensive experiments and analysis of DESO have been performed. The proposed algorithm presents superior computational efficiency and robustness compared with five state-of-the-art algorithms on benchmark problems from 20 to 200 dimensions. Besides, DESO is applied to an airfoil design problem to show its effectiveness.