Authors:
Chabi Babatounde
1
;
Bastien Poggi
1
;
Thierry Antoine-Santoni
1
and
Antoine Aiello
2
Affiliations:
1
Université de Corse, UMR CNRS 6134, Science Pour l’Environnement, Corte, France
;
2
Université de Corse, UMS CNRS 3514, STELLA MARE, Biguglia, France
Keyword(s):
Meta-heuristics, Optimization, Algorithm, Modeling and Simulation.
Abstract:
The research topic of the laboratory Science Pour l’Environnement (SPE) and the laboratory STELLA MARE of Université de Corse, focus on solving the environmental problems of our time. Various research teams focus their work on modeling and simulation of complex systems and behavioral modeling of species. Generally, in this modeling process (abstractions from the real world), we observe that the parameterization of the models is usually very tedious, carried out in an empirical or intuitive way based on assumptions specific to each modeler. There are also several modeling techniques which are generally parameterized intuitively and empirically. We have therefore proposed an approach to optimize the parameterization of models based on the algorithms of these models. This approach uses meta-heuristics, a class of optimization algorithms inspired by nature for which we obtain remarkable results. The use of meta-heuristics in this approach is justified by the nature of the problem to be s
olved. Indeed, the parameterization of models can be considered as a complex problem with a very large solution space that needs to be explored in an intelligent way. The risk of a combinatorial explosion is also very high because of the number of variables to be optimized. The advantage of this approach that we propose is that it allows an evolutive optimization of the model parameterization as the data arrives. For the validation of this approach, we used simulated data from a theoretical model. The validation of this theoretical model opens possibilities of applications on real world models.
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