Parallel versions of the mesh adaptive direct search algorithm

2026-07-09

AI Summary

This study addresses the challenges of high computational cost, time-consuming evaluations, and heterogeneity inherent in high-dimensional, multimodal black-box constrained optimization. It systematically investigates various parallel variants of the Mesh Adaptive Direct Search (MADS) algorithm, designing multi-level parallelization strategies tailored for multicore architectures to enable efficient synchronous evaluation of objective and constraint functions. The work comprehensively reviews and compares different parallelization mechanisms, elucidating their respective strengths and limitations. Furthermore, it provides reproducible implementations and empirical validation, demonstrating significant improvements in both solution efficiency and scalability of MADS for real-world problems.

Abstract

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.

Problem

• blackbox optimization
• mesh adaptive direct search
• parallel computing
• computation time
• constrained optimization

Innovation

• parallel MADS
• blackbox optimization
• constrained optimization
• multi-core parallelism
• mesh adaptive direct search