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 andlocal 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.

中文翻译：

---

数据驱动的进化抽样优化解决了昂贵的问题

代理模型已证明可有效地协助进化算法（EA）解决计算量大的复杂优化问题。但是，现有的代理辅助进化算法的有效性仍然需要提高。提出了一种数据驱动的进化抽样优化（DESO）框架，该框架在每一代随机采用两种进化抽样策略之一，即基于历史数据进行代理筛选和代理本地搜索，以有效地平衡全局搜索和本地搜索。在DESO中，径向基函数（RBF）被用作采样策略中的替代模型，并且使用不同程度的演化过程来采样候选点。通过采样策略对采样点进行评估，然后添加到数据库中，以在下一次采样中更新代理模型和总体。为了获得DESO的见识，已经对DESO进行了广泛的实验和分析。与从20到200维的基准问题的五种最新算法相比，该算法具有更高的计算效率和鲁棒性。此外，DESO应用于机翼设计问题以证明其有效性。