In many application areas, big optimization requires increasingly large-scale models to deal with a growing amount of decision variables and conflicting objectives, and subject to multiple sources of uncertainty. In this context, solving problems necessitates addressing the challenges of scalability and handling of uncertainty. This often requires revisiting not only the design and implementation of traditional optimization algorithms but also their parallelization on massively multi-core and heterogeneous (ultra-scale) supercomputers including in addition to multi-core processors, accelerators (e.g. GPU) and coprocessors (e.g. MIC).
The goal of BONUS is to come up with advanced approaches following three research topics constituting the roadmap of the project and which are the subject of several current and future collaborations of the team: decomposition-based optimization, optimization under uncertainty and ultra-scale optimization. The combined use of the three topics is rarely addressed in the literature. From a software point of view, the proposed approaches will mainly be integrated in the ParadisEO framework. In terms of application and industrial transfer, we target the scheduling of energy systems such as smart grids and engineering design.
Méta-modèles et apprentissage automatique pour l'optimisation massive
Sélection automatique d'algorithme pour l'optimisation multi-objectif
Massively Parallel Hybrid Surrogate-assisted Metaheuristics for Solving Expensive Optimization Problems
Conception, sélection et configuration d'algorithmes adaptifs pour l'optimisation inter-domaine
Methodologies for multiobjective parallel optilisatio, application to acrospace vehicle design
Machine Learning and Decomposition Technique for Large-scale Multi-objective Optimization
Optimisation robuste du crissement sous variabilités topographiques des surfaces de contact