Section: Partnerships and Cooperations

International Initiatives

Inria International Labs

Other IIL projects

• Title: Frontiers in Massive Optimization and Computational Intelligence (MODO)

• International Partner (Institution - Laboratory - Researcher): Shinshu University, Japan

• Start year: 2017

• See also: https://sites.google.com/view/lia-modo/

• Abstract: The aim of MODO is to federate French and Japanese researchers interested in the dimensionality, heterogeneity and expensive nature of massive optimization problems. The team receives a yearly support for international exchanges and shared manpower (joint PhD students).

Inria Associate Teams Not Involved in an Inria International Labs

• Title: Three-fold decomposition in multi-objective optimization (D${}^3$MO)

• International Partner (Institution - Laboratory - Researcher): University of Exeter, UK

• Start year: 2018

Inria International Partners

Informal International Partners

• School of Public Health and Preventive Medicine, Monash University, Australia (ranked 73^th over 1000 in the Shangai international ranking).

• Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Maracanaú, Brazil.

Participation in Other International Programs

• Title: Evolutionary many-objective optimization: application to smart cities and engineering design

• International Partner (Institution - Laboratory - Researcher): CINVESTAV-IPN, Mexico

• Start year: 2016

• Abstract: The project is co-funded by ECOS Nord, France and ANUIES, Mexico. It is focused on evolutionary many-objective optimization and its application to smart cities and engineering design.

• Title: Bridging the gap between exact methods and heuristics for multi-objective search (MOCO-Search)

• International Partner (Institution - Laboratory - Researcher): University of Coimbra and University of Lisbon, Portugal

• Start year: 2018

• Website: http://sites.google.com/view/moco-search/

• Abstract: This international project for scientific cooperation (PICS), funded by CNRS and FCT, aims to fill the gap between exact and heuristic methods for multi-objective optimization. The goal is to establish the link between the design principles of exact and heuristic methods, to identify features that make a problem more difficult to be solved by each method, and to improve their performance by hybridizing search strategies. Special emphasis is given to rigorous performance assessment, benchmarking, and general-purpose guidelines for the design of exact and heuristic multi-objective search.