Building upon Fast Multipole Methods to Detect and Model Organizations

Many models in natural and social sciences are comprised of sets of inter-acting entities whose intensity of interaction decreases with distance. This often leads to structures of interest in these models composed of dense packs of entities. Fast Multipole Methods are a family of methods developed to help with the calculation of a number of computable models such as described above. We propose a method that builds upon FMM to detect and model the dense structures of these systems

GraphStream: A Tool for bridging the gap between Complex Systems and Dynamic Graphs

The notion of complex systems is common to many domains, from Biology to Economy, Computer Science, Physics, etc. Often, these systems are made of sets of entities moving in an evolving environment. One of their major characteristics is the emergence of some global properties stemmed from local interactions between the entities themselves and between the entities and the environment. The structure of these systems as sets of interacting entities leads researchers to model them as graphs. However, their understanding requires most often to consider the dynamics of their evolution. It is indeed not relevant to study some properties out of any temporal consideration. Thus, dynamic graphs seem to be a very suitable model for investigating the emergence and the conservation of some properties. GraphStream is a Java-based library whose main purpose is to help researchers and developers in their daily tasks of dynamic problem modeling and of classical graph management tasks: creation, processing, display, etc. It may also be used, and is indeed already used, for teaching purpose. GraphStream relies on an event-based engine allowing several event sources. Events may be included in the core of the application, read from a file or received from an event handler

Modelling a Multi-Modal Logistic Network with Agents and Dynamic Graphs

International audienceThis paper presents a model of a logistic system. Our goal is to understand how such a system (with numerous stakeholders) behaves and evolves according to different constraints or scenarios. We adopted a complex system approach which leads us to propose an agent-based model coupled with dynamic graphs. It allows us to represent the properties, constraints and behaviours at a local level of a logistic system in order to reproduce the global behaviours thanks to the simulation in a dynamic context. The simulation (which uses data about the Seine axis) allows to test different scenarios in order to understand how local decisions impact the whole system. For example, this work presents the evolution of the system at the opening of the Seine-Nord Europe Canal. Indeed, this canal is a real major project for Europe, and has numerous economical stakes. So, we first describe the traffic evolution on the multi-modal transportation network (see figures 1 to 4). Then, we observe different other measures (evolution of costs, transportation mode share). Thanks to these analyses, we show that the Seine-Nord Europe Canal should promote the use of the river barges and reduce financial costs. In the same time, it could modify the respective shares of the northern European ports

Distribution dynamique adaptative à l'aide de mécanismes d'intelligence collective

This work presents a dynamic adaptive distribution method for distributed applications made of large number of interacting entities in a versatile computation environment. Load balancing as well as communication minimization are taken into account. The proposed method is based on the detection of organizations inside the application to better distribute it. Organizations are identified as groups of highly communicating entities. Organizations evolve, appear, strengthen, weaken and disappear. Available computing resources where the application runs also change. Such constraints dictate that the distribution be dynamic and adaptive. The method is based on colonies of numerical ants trying to gather entities of the application. Ants cooperate inside a unique colony and compete when they are not in the same colony. They try to capture organizations inside the application, each colony working for a distinct computing resource. Competition between colonies allow the load balancing. Collaboration inside colonies allow to detect organizations, putting highly communicating sets on the same computing resource. Finally, population management allow to take into account computing resources heterogeneity.Ce travail présente une méthode de distribution dynamique et adaptative, pour des applications distribuées constituées de multiples entités en interaction, dans un environnement de calcul versatile. L'équilibrage de charge ainsi que la minimisation des coûts de communication sont pris en compte. La méthode proposée repose sur la détection d'organisations au sein de l'application afin de mieux la distribuer. Les organisations sont identifiées comme des groupes d'entités en très forte communication. Les organisations évoluent, apparaissent, se renforcent, s'affaiblissent et disparaissent. Les ressources disponibles de calcul sur lesquelles l'application s'exécutent varient également. Ces contraintes imposent à la distribution de s'adapter dynamiquement. La méthode est basée sur des colonies de fourmis numériques qui tentent de recruter les entités de l'application. Les fourmis coopèrent au sein d'une même colonie et sont en compétition lorsqu'elles n'appartiennent pas à une même colonie. Elles tentent de s'approprier les organisations au sein de l'application, chaque colonie travaillant pour une ressource de calcul distincte. La compétition inter-colonies permet la répartition de la charge. La collaboration au sein de chaque colonie permet la détection des organisations, en plaçant les très fortes communications ensembles sur la même ressource de calcul. Enfin la gestion de la population permet de prendre en compte l'hétérogénéité des ressources de calcul

Résolution de problèmes en essaim

International audienc

Swarm Problem-Solving

International audienceIt is increasingly common for algorithms in computer science to be inspired by “natural” models. This is not a new trend. Computer science has always drawn from its surroundings as a source of inspiration and our user interfaces are proof of this. Examples of algorithms and programming models like this include, among others, simulated annealing, cellular automata, DNA computing, evolutionary algorithms and artificial chemistry

Distribution dynamique adaptative à l'aide de mécanisme d'intelligence collective (détection d'organisations par des techniques de collaboration et de compétition)

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