EXPObench

EXPObench是由代尔夫特理工大学的研究团队创建的一个用于评估代理优化算法性能的数据集。该数据集包含四个昂贵的黑盒优化问题，这些问题来自不同的实际应用领域，如风电场布局优化和管道形状优化等。数据集旨在通过提供标准化的测试环境，帮助研究人员和工程师更好地理解和比较不同代理算法的性能。EXPObench数据集的创建，为代理优化算法的研究提供了一个重要的基准，有助于推动该领域的技术进步。

EXPObench is a dataset created by a research team from Delft University of Technology for evaluating the performance of surrogate optimization algorithms. It contains four expensive black-box optimization problems derived from diverse real-world application domains, such as wind farm layout optimization and pipeline shape optimization. The dataset aims to help researchers and engineers better understand and compare the performance of different surrogate algorithms by providing a standardized test environment. The creation of the EXPObench dataset provides a critical benchmark for surrogate optimization algorithm research, facilitating technological progress in this field.