Use of surrogates in derivative-free optimization

在无导数优化中使用代理

• 批准号: 418250-2012
• 负责人: LeDigabel, Sébastien
• 金额: $ 1.89万
• 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: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=551309
• 关键词: Use; surrogates; derivative; free; optimization

The research project described in this proposal concerns derivative-free optimization (DFO). More precisely, it focuses on blackbox optimization, which occurs when the objective(s) and constraints of an engineering optimization problem are obtained by a computer code seen as a blackbox. Several characteristics make these codes impractical for optimization: they may be expensive to evaluate, be contaminated with noise, or fail to return a value. No derivative information is available and even approximations can not be exploited for the optimization. In this context, derivative-based methods cannot be used, and DFO methods may be considered. The present proposal discusses extensions of the mesh adaptive direct search (MADS) method, and in particular the use of surrogates to improve its efficiency.

该提案中描述的研究项目涉及无衍生品优化（DFO）。更确切地说，它的重点是黑框优化，这是在目标（S）和工程优化问题的约束时发生的，该计算机代码被视为黑框。这些特征使这些代码不切实际以进行优化：它们可能很昂贵，评估，被噪声污染或无法返回值。没有可用的衍生信息，甚至无法利用近似值进行优化。在这种情况下，不能使用基于导数的方法，并且可以考虑DFO方法。本提案讨论了网格自适应直接搜索方法（MADS）方法的扩展，尤其是使用替代物来提高其效率。