Parallel versions of the mesh adaptive direct search algorithm
For practitioners dealing with computationally expensive blackbox optimization problems, this survey provides a comprehensive overview of parallel MADS strategies to reduce computation time.
This paper surveys parallel variants of the mesh adaptive direct search (MADS) algorithm for constrained blackbox optimization, detailing implementations, computational results, and insights into advantages and limitations of each method.
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.