Vehicular mobility model optimization using cooperative coevolutionary genetic algorithms

Abstract

A key factor for accurate vehicular ad hoc networks (VANET) simulation is the quality of its underlying mobility model. VehILux is a recent vehicular mobility model that generates traces using traffic volume counts and real-world map data. This model uses probabilistic attraction points which values require optimization to provide realistic traces. Previous sensitivity analysis and application of genetic algorithms (GAs) on the Luxembourg problem instance have outlined this model's limitations. In this article, we first propose an extension of the model using a higher number of auto-generated attraction points. Then its decomposition on the Luxembourg instance using geographical information is proposed as a way to break epistatic links and hence make its optimization using cooperative coevolutionary genetic algorithms (CCGAs) more efficient. Experimental results demonstrate the significant realism increase brought by both the VehILux model enhancements and the CCGA compared to the generational and cellular GAs.

DOI: 10.1145/2463372.2463539

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Cite this paper

@inproceedings{Nielsen2013VehicularMM, title={Vehicular mobility model optimization using cooperative coevolutionary genetic algorithms}, author={Sune S. Nielsen and Gr{\'e}goire Danoy and Pascal Bouvry}, booktitle={GECCO}, year={2013} }