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.



  • Professors
    • Nouredine Melab (Responsable)
    • El-Ghazali Talbi
  • Associate professors
    • Omar Abdelkafi
    • Bilel Derbel
    • Arnaud Liefooghe


  • Postdoc
    • Mohammad Rahimi
  • Phd students
    • Nicolas Berveglieri
    • Sohrab Faramarzi Oghani
    • Ali Hebbal
    • Julien Pelamatti
    • Geoffrey Pruvost

Nicolas Berveglieri

Méta-modèles et apprentissage automatique pour l'optimisation massive

Sohrab Faramarzi Oghani

Optimisation de laboratoires d'analyse : de la modélisation à la résolution du problème

Ali Hebbal

Methodologies for multiobjective parallel optilisatio, application to acrospace vehicle design

Julien Pelamatti

Optimisation multidisciplinaire de lanceurs avec prise en compte de choix technologiques

Geoffrey Pruvost

Machine Learning and Decomposition Technique for Large-scale Multi-objective Optimization

Jan Gmys

Heterogeneous cluster computing for many-task exact optimization – Application to permutation problems 2017-12-19

Bilel Derbel

Contributions to single- and multi- objective optimization: towards distributed and autonomous massive optimization 2017-12-11

Other ' OPTIMA : OPTImisation : Modèles et Applications ' teams