Data-driven evolutionary algorithms (DDEAs) aim to utilize data and surrogates to drive optimization, which is useful and efficient when the objective function of the optimization problem is expensive or difficult to access. However, the performance of DDEAs relies on their surrogate quality and often deteriorates if the amount of available data decreases. To solve these problems, this article proposes a new DDEA framework with perturbation-based ensemble surrogates (DDEA-PES), which contain two efficient mechanisms. The first is a diverse surrogate generation method that can generate diverse surrogates through performing data perturbations on the available data. The second is a selective ensemble method that selects some of the prebuilt surrogates to form a final ensemble surrogate model. By combining these two mechanisms, the proposed DDEA-PES framework has three advantages, including larger data quantity, better data utilization, and higher surrogate accuracy. To validate the effectiveness of the proposed framework, this article provides both theoretical and experimental analyses. For the experimental comparisons, a specific DDEA-PES algorithm is developed as an instance by adopting a genetic algorithm as the optimizer and radial basis function neural networks as the base models. The experimental results on widely used benchmarks and an aerodynamic airfoil design real-world optimization problem show that the proposed DDEA-PES algorithm outperforms some state-of-the-art DDEAs. Moreover, when compared with traditional nondata-driven methods, the proposed DDEA-PES algorithm only requires about 2% computational budgets to produce competitive results.

中文翻译：

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具有基于扰动的集成代理的数据驱动进化算法

数据驱动的进化算法 (DDEA) 旨在利用数据和代理来驱动优化，当优化问题的目标函数成本高昂或难以访问时，这非常有用且有效。但是，DDEA 的性能取决于其代理质量，并且如果可用数据量减少，则性能通常会下降。为了解决这些问题，本文提出了一种新的基于扰动的集成代理（DDEA-PES）的DDEA框架，它包含两种有效的机制。第一种是多样化的代理生成方法，可以通过对可用数据进行数据扰动来生成多样化的代理。第二种是选择性集成方法，它选择一些预先构建的代理来形成最终的集成代理模型。通过结合这两种机制，提出的 DDEA-PES 框架具有三个优点，包括数据量更大、数据利用率更好、代理准确率更高。为了验证所提出框架的有效性，本文提供了理论和实验分析。为了进行实验比较，以遗传算法为优化器，径向基函数神经网络为基础模型，开发了具体的DDEA-PES算法作为实例。广泛使用的基准和气动翼型设计实际优化问题的实验结果表明，所提出的 DDEA-PES 算法优于一些最先进的 DDEA。此外，与传统的非数据驱动方法相比，所提出的 DDEA-PES 算法只需要大约 2% 的计算预算即可产生有竞争力的结果。