Use of surrogates in derivative-free optimization

该方案中描述的研究项目涉及无导数优化(DFO)。更准确地说，它关注的是黑箱优化，当工程优化问题的目标(S)和约束通过被视为黑箱的计算机代码获得时发生。有几个特点使这些代码不适用于优化：它们的评估成本可能很高，受噪声污染，或者无法返回值。没有可获得的导数信息，甚至不能利用近似来进行优化。在这种情况下，不能使用基于导数的方法，可以考虑使用DFO方法。本提案讨论了网格自适应直接搜索(MADS)方法的扩展，特别是使用代理来提高其效率。

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.