AI 中文总结
针对约束黑箱优化问题计算成本高的情况，研究MADS算法的并行版本，采用不同并行程度和策略，详细介绍实际实现、给出计算结果，探讨了各并行方法优缺点。
AI 中文摘要
本文研究了用于约束黑箱优化的网格自适应直接搜索（MADS）算法的不同并行变体。由于搜索空间可能存在大量变量和多模态，这些问题本质上意味着高计算成本。此外，定义问题的黑箱潜在的时间密集性和时间异质性促使需要高效实现。并行性成为减少计算时间的可行解决方案。文中回顾的方法采用不同程度的并行性和不同的并行策略来有效解决上述各方面问题。本文详细介绍了实际实现，给出了计算结果，并深入探讨了每种MADS并行方法的优缺点。
英文摘要
This work surveys the different parallel variants of the mesh adaptive direct search (MADS) algorithm for constrained blackbox optimization. These problems can inherently imply high computational costs due to the possible large number of variables and multi-modality of the search space. In addition, the potential time-intensive nature and time heterogeneity of the blackboxes defining the problem prompts the need for efficient implementations. Parallelism emerges as an actionable solution to mitigate computation time, as modern computer systems rely on multi-core architecture. The reviewed methods employ diverse levels of parallelism and distinct parallel strategies to effectively tackle each aspect outlined above. The manuscript details the practical implementations, provides computational results, and offers insights into the advantages and limitations of each MADS parallel method.
