  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.
