CoBRA: A Coevolutionary Meta-heuristic for Bi-level Optimization

This article presents CoBRA, a new parallel coevolutionary algorithm for bi-level optimization. CoBRA is based on a coevolutionary scheme to solve bi-level optimization problems. It handles population-based meta-heuristics on each level, each one cooperating with the other to provide solutions for the overall problem. Moreover, in order to evaluate the relevance of CoBRA against more classical approaches, a new performance assessment methodology, based on rationality, is introduced. An experimental analysis is conducted on a bi-level distribution planning problem, where multiple manufacturing plants deliver items to depots, and where a distribution company controls several depots and distributes items from depots to retailers. The experimental results reveal significant enhancements with respect to a more classical approach, based on a hierarchical scheme.Cet article présente CoBRA, un nouvel algorithme paralléle et coévolutionnaire pour l'optimisation bi-niveau. CoBRA se base sur un modèle coévolutionnaire pour faire face aux problèmes d'optimisation bi-niveau. Il manipule une méta-heuristique à base de population sur chaque niveau, chacune coopérant avec l'autre de manière à garder une vue générale sur le problème complet. De plus, afin d'étudier la pertinence de CoBRA par rapport aux approches plus classique, une nouvelle méthodologie, basée sur la rationalité est introduite. Est conduite ensuite une étude expérimentale sur un problème bi-niveau de distribution-production, dans lequel des usines contrôlées par une entreprise produisent des marchandises pour des dépôts, et une autre entreprise contrôlant les dépôts se charge de livrer les marchandises à des clients. Cet article se conclut sur l'observation d'un réel gain de performance par rapport à une approche plus classique, basée sur un modèle hiérarchique

CoBRA: A cooperative coevolutionary algorithm for bi-level optimization

International audienceThis article presents CoBRA, a new evolutionary algorithm, based on a coevolutionary scheme, to solve bi-level optimization problems. It handles population-based algorithms on each level, each one cooperating with the other to provide solutions for the overall problem. Moreover, in order to evaluate the relevance of CoBRA against more classical approaches, a new performance assessment methodology, based on rationality, is introduced. An experimental analysis is conducted on a bi-level distribution planning problem, where multiple manufacturing plants deliver items to depots, and where a distribution company controls several depots and distributes items from depots to re- tailers. The experimental results reveal significant enhancements, particularly over the lower level, with respect to a more classical approach based on a hierarchical scheme

Cost minimization of service deployment in a public cloud environment

International audienc

Cost minimization of service deployment in a multi-cloud environment

International audiencePublic cloud computing allows one to rent virtual servers on a hourly basis. This raises the problematic of being able to decide which server offers to take, which providers to use, and how to use them to acquire sufficient service capacity, while maintaining a cost effective platform. This article proposes a new realistic model to tackle the problem, placing services into IAAS virtual machines from multiple providers. A flexible protocol is defined to generate real-life instances, and applied on two industrial cases with four real cloud providers. An evolutionary approach, with new specific operators, is introduced and compared to a MIP formulation. Experiments conducted on two data-sets show that the evolutionary approach is viable to tackle real-size instances in reasonable amount of time

A Multi-objective Evolutionary Algorithm for Cloud Platform Reconfiguration

International audienceOffers of public IAAS providers often vary: new providers enter the market, existing ones change their pricing or improve their offering. Decision on whether and how to improve already deployed platforms, either by reconfiguration or migration to another provider, can be seen as a NP-hard optimization problem. In this paper, we define a new realistic model for this Migration Problem, based on a Multi-Objective Optimization formulation. An evolutionary approach is introduced to tackle the problem, using specific operators. Experiments are conducted on multiple realistic data-sets, showing that the evolutionary approach is viable to tackle real-size instances in a reasonable amount of time