Adaptative Dichotomic Optimization: a New Method for the Calibration of Agent-Based Models
Résumé
In this paper, we propose a new approach for the exploration of the parameter space of agent-based models: Adaptative Dichotomic Optimization. Agent-based models are generally characterized by a great number of parameters, a lot of which cannot be evaluated with the current knowledge about the real system. The aim of the work is to provide tools for the calibration of these models, which consists in finding the optimal set of parameters for a given criterion. The criterion can be for example that the model achieves a specific function optimally or that the results of the simulation are as close to possible of experimental data. Our approach is based on the partition of the parameter space (the interval of variation of each variable is divided into a finite number of intervals) and on a parallel exploration of the various parameters by the agents of the model. The navigation in the parameter space is done by grouping or dividing adaptively some of the intervals, according to an algorithm which is adapted from Ant Colony Systems.
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