Derivative-free and blackbox optimization for engineering problems

针对工程问题的无导数和黑盒优化

• 批准号: RGPIN-2015-05311
• 负责人: Audet, Charles
• 金额: $ 2.48万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636798
• 关键词: Derivative; free; blackbox; optimization; engineering

This research project is the continuation of my prior work funded by NSERC on the development of the Mesh Adaptive Direct Search algorithm (MADS) for derivative-free and blackbox optimization problems.  In many engineering optimization problems, the objective and constraint functions are nonsmooth and evaluated through a computer simulation. The outputs of these codes are often of limited precision, contaminated with numerical noise, expensive to evaluate, and sometimes the code fails to return a value.

该研究项目是NSERC资助的关于网格自适应直接搜索算法（MADS）的先前工作的延续。在许多工程优化问题中，目标和约束功能是非平滑的，并通过计算机模拟进行了评估。这些代码的输出通常受到限制的精度，被数值噪声污染，评估昂贵，有时代码无法返回值。