BONUS team


Leader: Nouredine Melab



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.


Nicolas Berveglieri

Méta-modèles et apprentissage automatique pour l'optimisation massive

Alexandre Borges de Jesus

Sélection automatique d'algorithme pour l'optimisation multi-objectif

Guillaume Briffoteaux

Massively Parallel Hybrid Surrogate-assisted Metaheuristics for Solving Expensive Optimization Problems

Lorenzo Canonne

Optimisation massivement parallèle boîte grise et large échelle

Raphaël Cosson

Conception, sélection et configuration d'algorithmes adaptifs pour l'optimisation inter-domaine

Thomas Firmin

Optimisation Bayésienne Parallèle des Réseaux Neuromorphiques

Guillaume Helbecque

Productivity-aware parallel cooperative combinatorial optimization for ultra-scale supercomputers

Julie Keisler

Optimisation de modèles Deep Learning pour la prévision spatio-temporelle

Houssem Ouertatani

Optimisation multi-critères et conception automatisée des réseaux de neurones profonds

David Redon

Attaquer la "large échelle" : calcul haute performance pour l'intelligence computationnelle

Jérémy Sadet

Optimisation robuste du crissement sous variabilités topographiques des surfaces de contact

Les autres équipes du groupe thématique ' OPTIMA '