Performance Comparison of Surrogate-Assisted Evolutionary Algorithms on Computational Fluid Dynamics Problems
This work addresses the lack of benchmarking on real-world problems for researchers in optimization and computational fluid dynamics, though it is incremental as it focuses on performance comparison rather than introducing new methods.
The paper compared eleven state-of-the-art surrogate-assisted evolutionary algorithms on two real-world computational fluid dynamics problems, finding that more recent methods and those using differential evolution performed significantly better.
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.