Efficacité des heuristiques de branchement pour le branch-and-bound multi-objectif : vers une gestion plus dynamique

National audienceLes problèmes d'optimisation combinatoire multi-objectif sont réputés pour être particulièrement difficiles à résoudre efficacement. Parmi les approches de résolution possibles, les algorithmes de branch-and-bound sont largement utilisés comme méthodes exactes, fondées sur un parcours arborescent de l’espace des solutions. Une des principales composantes de ces algorithmes est la stratégie de branchement, qui sélectionne à chaque étape de séparation la variable à instancier dans les sous-problèmes résultants. Pour un problème donné, il existe généralement plusieurs heuristiques de choix de la variable de séparation, les performances de ces heuristiques peuvent différer d'une instance à l'autre et il n'est souvent pas possible de définir une heuristique qui s’avère la plus performante sur l’ensemble des instances (cf. No Free Lunch Theorems). Classiquement les algorithmes de branch-and-bound appliquent une seule heuristique, fixe toute au long de la résolution..Dans ce travail nous cherchons à déterminer si l'application conjointe de plusieurs heuristiques lors d'une même résolution permet d'augmenter l'efficacité de l'algorithme. Nous nous intéressons plus particulièrement aux stratégies de branchement pour le problème du sac-à-dos binaire bi-objectif. Les heuristiques de branchement pour ce problème sont nombreuses, considérant soit un seul des objectifs, soit un compromis des deux objectifs. Dans un premier temps, nous tentons de mettre en évidence les forces et faiblesses de ces différentes heuristiques en fonction des instances, dans le but d'élaborer une stratégie statique mêlant plusieurs heuristiques. La diversité des instances rend cette tâche particulièrement difficile. Toutefois, nous sommes parvenus à montrer que la combinaison de différentes stratégies de branchement permet de réduire la taille de l'arbre de recherche. Nous avons ensuite défini des mesures de qualité pour ces heuristiques, que nous utilisons via un mécanisme d’apprentissage automatique pour sélectionner dynamiquement la stratégie de branchement à chaque séparation au cours du processus de branch-and-bound. Finalement, nous comparons l'efficacité de ce nouvel algorithme par rapport à l'emploi d'une stratégie unique de séparation et analysons les différents réglages de cette approche adaptative

A branch-and-cut method for the bi-objective bi-dimensional knapsack problem

International audienceMulti-objective multi-dimensional knapsack problems (pOmDKP) are widely used to represent practical problems as capital budgeting or allocating processors. It aims to select a subset of n items such that the sum of weight of the selected items does not exceed the capacity on any of the m dimensions, while maximizing p objective functions. Each item has a weight on each dimension and a profit for each objective function. This problem is known for being particularly difficult as soon as the number of dimensions exceeds one, even in its single-objective version.There are many published papers focusing on the exact solution of multi-objective single-dimensional knapsack. The solutions methods are often two-phases methods. The second phase is either a branch-and-bound method (as in [1] for the bi-objective case or in [2] for the three-objective case), either a dynamic programming method [3], or a dedicated ranking method [2].Only a few works have studied exactly the multi-objective multi-dimensional case. Concerning the single-objective multi-dimensional knapsack problem, many works have investigated cutting inequalities to speed-up the computation of solution [4].In this work we are interested in the exact solution of the bi-objective bi-dimensional knapsack problem (2O2DKP), using a branch-and-cut method. A branch-and-cut method is a combination of a cutting plane method and a branch-and-bound method. According to its name, one of the main component of a branch-and-bound method aims at computing bounds of the problem. Convex relaxation has been a key component for successful bi-objective branch-and-bound algorithm (see for example [5]). It defines indeed a tight upper bound set, which can be computed easily if the single-objective version of the problem can be solved in (pseudo-)polynomial time. However, this is not the case for 2O2DKP. On the contrary, the linear relaxation remains relatively easy to compute, but the resulting bound set is less tight, which makes more difficult the exploration of nodes and leads to larger search-trees. To improve the quality of the upper bound set based on linear relaxation, we introduce cover inequalities at each node of the branch-and-bound method, turning it to a branch-and-cut method. Cover inequalities consist of cuts defined for single-objective binary problems [6]. A cover is a set of objects such that the sum of the weights associated to these objects exceeds the capacity. In [6], the authors remark that computing all possible cover inequalities would be time-consuming and even impossible to implement. Instead, they consider the optimal solution of the linear relaxation and solve a smaller binary problem to find a cover inequality that is violated. In the bi-objective context, the linear relaxation is described by a set of extreme points, which are associated to efficient solutions. Moreover, each of these efficient solutions may be fractional and have a different subset of fractional variables. The generation of cover inequalities is therefore more complex, particularly to get a good tradeoff between quality of the improved upper bound set defined and computational time. This leads to numerous strategies to generate cover inequalities. This presentation will describe the mechanisms used in the multi-objective branch-and-cut method that we have developed (separation procedure, bound sets, generation of cover inequalities...). These strategies have been then experimentally validated. [1] Visée, M., Teghem, J., Pirlot, M., Ulungu, E. L., March 1998. Two-phases method and branch and bound procedures to solve the bi–objective knapsack problem. Journal of Global Optimization 12, 139–155. [2] Jorge, J., May 2010. Nouvelles propositions pour la résolution exacte du sac à dos multi-objectif unidimensionnel en variables binaires. Thèse, Université de Nantes.[3] Delort, C., Spanjaard, O., 2010. Using bound sets in multiobjective optimization: Application to the biobjective binary knapsack problem. In: Festa, P. (Ed.), SEA. Vol 6049 of Lecture Notes in Computer Science. Springer, 253-265.[4] Osorio, M. A., Glover, F., Hammer, P., 2002. Cutting and surrogate constraint analysis for improved multidimensional knapsack solutions. Annals of Operations Research 117 (1-4), 71–93.[5] Sourd F. and Spanjaard O., 2008. A multi-objective branch-and bound framework: Application to the biobjective spanning tree problem. INFORMS Journal on Computing, 20:472-484.[6] Crowder, H., Johnson, E. L., Padberg, M. W., 1983. Solving large-scale zero-one linear programming problems. Operations Research 31 (5), 803–834

Toward privacy in IoT mobile devices for activity recognition

International audienceRecent advances in wireless sensors for personal healthcare allow to recognise human real-time activities with mobile devices. While the analysis of those datastream can have many benefits from a health point of view, it can also lead to privacy threats by exposing highly sensitive information. In this paper, we propose a privacy-preserving framework for activity recognition. This framework relies on a machine learning technique to efficiently recognise the user activity pattern, useful for personal healthcare monitoring, while limiting the risk of re-identification of users from biometric patterns that characterizes each individual. To achieve that, we first deeply analysed different features extraction schemes in both temporal and frequency domain. We show that features in temporal domain are useful to discriminate user activity while features in frequency domain lead to distinguish the user identity. On the basis of this observation, we second design a novel protection mechanism that processes the raw signal on the user's smartphone and transfers to the application server only the relevant features unlinked to the identity of users. In addition, a generalisation-based approach is also applied on features in frequency domain before to be transmitted to the server in order to limit the risk of re-identification. We extensively evaluate our framework with a reference dataset: results show an accurate activity recognition (87%) while limiting the re-identifation rate (33%). This represents a slightly decrease of utility (9%) against a large privacy improvement (53%) compared to state-of-the-art baselines

Inhibition of colon cancer growth by docosahexaenoic acid involves autocrine production of TNFα

IF 7.932International audienceThe omega-3 polyunsaturated fatty acid docosahexaenoic acid (DHA) has anti-inflammatory and anti-cancer properties. Among pro-inflammatory mediators, tumor necrosis factor a (TNF alpha) plays a paradoxical role in cancer biology with induction of cancer cell death or survival depending on the cellular context. The objective of the study was to evaluate the role of TNFa in DHA-mediated tumor growth inhibition and colon cancer cell death. The treatment of human colorectal cancer cells, HCT-116 and HCT-8 cells, with DHA triggered apoptosis in autocrine TNF alpha-dependent manner. We demonstrated that DHA-induced increased content of TNF alpha mRNA occurred through a post-transcriptional regulation via the down-regulation of microRNA-21 (miR-21) expression. Treatment with DHA led to nuclear accumulation of Foxo3a that bounds to the miR-21 promoter triggering its transcriptional repression. Moreover, inhibition of RIP1 kinase and AMP-activated protein kinase a reduced Foxo3a nuclear-cytoplasmic shuttling and subsequent increase of TNFa expression through a decrease of miR-21 expression in DHA-treated colon cancer cells. Finally, we were able to show in HCT-116 xenograft tumor-bearing nude mice that a DHA-enriched diet induced a decrease of human miR-21 expression and an increase of human TNF alpha mRNA expression limiting tumor growth in a cancer cell-derived TNF alpha dependent manner. Altogether, the present work highlights a novel mechanism for anti-cancer action of DHA involving colon cancer cell death mediated through autocrine action of TNF alpha

Dynamic Adaptation of the Traffic Management System CarDemo

2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems, SASO, London, UK, 8-12 September, 2014This paper demonstrates how we applied a constraint-based dynamic adaptation approach on CarDemo, a traffic management system. The approach allows domain experts to describe the adaptation goals as declarative constraints, and automatically plan the adaptation decisions to satisfy these constraints. We demonstrate how to utilise this approach to realise the dynamic switch of routing services of the traffic management system, according to the change of global system states and user requests.Science Foundation IrelandLer

Balancing and Configuration Planning of RMS to Minimize Energy Cost

International audienceIn this paper, we investigate the use of the scalability property of RMS to reduce the energy cost during the production. The corresponding optimization problem is a new Bilevel Optimization problem which combines a line balancing problem with a planning problem. A heuristic based on a simulated annealing algorithm and a linear program is proposed. An illustrative example is presented to highlight the potential of this new approach compared to the cost obtained with a classic production line