Surrogate-assisted evolutionary algorithms (SAEAs) are recently among the most widely studied methods for their capability to solve expensive real-world optimization problems. However, the development of new methods and benchmarking with other techniques still relies almost exclusively on artificially created problems. In this paper, we use two real-world computational fluid dynamics problems to compare the performance of eleven state-of-the-art single-objective SAEAs. We analyze the performance by investigating the quality and robustness of the obtained solutions and the convergence properties of the selected methods. Our findings suggest that the more recently published methods, as well as the techniques that utilize differential evolution as one of their optimization mechanisms, perform significantly better than the other considered methods.

翻译：代理辅助进化算法（SAEAs）因其解决昂贵实际优化问题的能力，近年来成为最广泛研究的方法之一。然而，新方法的开发及其与其他技术的基准测试仍几乎完全依赖人工创建的问题。本文利用两个真实世界的计算流体动力学问题，比较了十一种最先进的单目标SAEAs的性能。我们通过分析所得解的质量与鲁棒性以及所选方法的收敛特性来评估性能。研究结果表明，近期发表的方法，以及那些将差分进化作为其优化机制之一的技术，其表现显著优于其他被考虑的方法。