Use of surrogates in derivative-free optimization
|Numéro de l'application :
|Année de concours :
|Année financière :
|Nom de la personne :
|École Polytechnique de Montréal
|Mathématiques et génie industriel
|26 000 $
|1 - 5
|Type de programme :
|Programme de subventions à la découverte - individuelles
|Comité évaluateur :
|Génie civil, industriel et des systèmes
|Sujet de recherche :
|Domaine d'application :
|Chercheurs associés :
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.
My research is equally divided into three categories: algorithmic developments, optimization software design, and applications. The present application proposes six projects in the first category. Support for the two other categories has been requested from other organizations.
Most of these six projects concern the use of surrogates within a direct search framework. Each project, in terms of objectives and time frame, has been specifically defined for an MSc or PhD student. As a consequence, approximately 86% of the requested budget is devoted to graduate-student salaries.
- Date de modification :