Using Meta-heuristics to Optimize the Parameterization of Algorithms in Simulation Models

Chabi Babatounde, Bastien Poggi, Thierry Antoine-Santoni, Antoine Aiello

2021

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 solved. 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.

Paper Citation

in Harvard Style

Babatounde C., Poggi B., Antoine-Santoni T. and Aiello A. (2021). Using Meta-heuristics to Optimize the Parameterization of Algorithms in Simulation Models. In Proceedings of the 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH, ISBN 978-989-758-528-9, pages 215-223. DOI: 10.5220/0010508102150223

in Bibtex Style

@conference{simultech21,
author={Chabi Babatounde and Bastien Poggi and Thierry Antoine-Santoni and Antoine Aiello},
title={Using Meta-heuristics to Optimize the Parameterization of Algorithms in Simulation Models},
booktitle={Proceedings of the 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,},
year={2021},
pages={215-223},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010508102150223},
isbn={978-989-758-528-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,
TI - Using Meta-heuristics to Optimize the Parameterization of Algorithms in Simulation Models
SN - 978-989-758-528-9
AU - Babatounde C.
AU - Poggi B.
AU - Antoine-Santoni T.
AU - Aiello A.
PY - 2021
SP - 215
EP - 223
DO - 10.5220/0010508102150223