Couplage d'algorithmes d'optimisation par un système multi-agents pour l'exploration distribuée de simulateurs complexes : application à l'épidémiologie

Abstract : Study of complex systems such as environmental or urban systems, often requires the use of simulators for understanding the dynamics observed or getting a prospective vision of the evolution of system. However, the credit given to results of a simulation depends heavily on the trust placed in the simulator, and the quality of validation. This trust is achieved only through an advanced study on the model, a sensitivity analysis of parameters and a comparison of simulation results and collected data. For all of those, plethora of simulations is necessary, which is costly in term of computing resources (CPU time, memory and processors) and is often impossible because of the size of parameters space. It is therefore important to reduce significantly and intelligently the domain to explore. One of the special properties of representative simulators of real phenomena is that they own a parameters space, of which the nature and the form is based on: (i) the scientific objectives; (ii) the nature of manipulated parameters; and (iii) especially complex systems. Thus, the choice of an exploration strategy is totally dependent on the domain to explore. The generic algorithms in the literature are then not optimal. Because of the singularity of complex simulators, the necessities and the difficulties of exploring their parameters space, we plan to guide the exploration task of complex systems by providing GRADEA, a stratified cooperative exploration protocol, that hybrids three different algorithms of different categories in the same environment: the screening search for areas of interest, the global search and the local search. Various exploration algorithms will explore the search space by parallel manner to find the global optimum of optimization problem and also to partially describe the cartography of solutions space to understand the emergent behavior of the model. The first results of the stratified exploration protocol with an example of preselected search algorithms are applied to the environmental simulator for the design of vaccination policies of measles disease in Vietnam. The coupling of search algorithms is built on a modular and agent based architecture that interacts with a computing cluster where the simulations run. This environment facilitates both the interaction between a group of search algorithms, and also the use of high performance computing resources. The challenge is resolved to propose to community, an optimized environment where users will be able: (i) to combine search algorithms that adapted to case study; (ii) and take advantage of the available resources of high performance computing to accelerate the exploration.
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The Nhan Ho. Couplage d'algorithmes d'optimisation par un système multi-agents pour l'exploration distribuée de simulateurs complexes : application à l'épidémiologie. Calcul parallèle, distribué et partagé [cs.DC]. Université Pierre et Marie Curie - Paris VI, 2016. Français. ⟨NNT : 2016PA066547⟩. ⟨tel-01531905⟩

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