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.
- The cabspotting project, {online}. http://cabspotting.org/.Google Scholar
- OpenStreetMap project, {online}. http://www.openstreetmap.org/.Google Scholar
- Traffic volume counts in Luxembourg, Luxembourg ministry of transportation, {online}. http://www.pch.public.lu/trafic/comptage/index.html.Google Scholar
- E. Alba and B. Dorronsoro. Cellular Genetic Algorithms. Operations Research/Compuer Science Interfaces. Springer-Verlag Heidelberg, 2008. Google ScholarDigital Library
- M. Behrisch, L. Bieker, J. Erdmann, and D. Krajzewicz. SUMO - Simulation of Urban MObility: An Overview. In Proc. 3rd International Conference on Advances in System Simulation, SIMUL, pages 63--68, Spain, 2011.Google Scholar
- T. Camp, J. Boleng, and V. Davies. A survey of mobility models for ad hoc network research. Wireless Communications and Mobile Computing, 2(5):483--502, 2002.Google ScholarCross Ref
- C. Gawron. An iterative algorithm to determine the dynamic user equilibrium in a traffic simulation model. International Journal of Modern Physics C, 9(3):393--407, 1998.Google ScholarCross Ref
- A. Grzybek, G. Danoy, and P. Bouvry. Generation of realistic traces for vehicular mobility simulations. In Proceedings of the second ACM international symposium on Design and analysis of intelligent vehicular networks and applications, DIVANet '12, pages 131--138, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- V. Naumov, R. Baumann, and T. Gross. An evaluation of inter-vehicle ad hoc networks based on realistic vehicular traces. In 7th ACM international symposium on Mobile ad hoc networking and computing, MobiHoc '06, pages 108--119, 2006. Google ScholarDigital Library
- Y. Pigné, G. Danoy, and P. Bouvry. Sensitivity analysis for a realistic vehicular mobility model. In ACM international symposium on Design and analysis of intelligent vehicular networks and applications (DIVANet 2011), pages 31--38. ACM, 2011. Google ScholarDigital Library
- Y. Pigné, G. Danoy, and P. Bouvry. A vehicular mobility model based on real traffic counting data. In Proc. 3rd International Workshop on Communication Technologies for Vehicles (Nets4Cars 2011), volume 6596, pages 131--142. Springer, LNCS, 2011. Google ScholarDigital Library
- M. Potter and K. De Jong. A cooperative coevolutionary approach to function optimization. In Parallel Problem Solving from Nature (PPSN III), pages 249--257. Springer, 1994. Google ScholarDigital Library
- M. A. Potter. The design and analysis of a computational model of cooperative coevolution. PhD thesis, 1997. Google ScholarDigital Library
- M. Seredynski, G. Danoy, M. Tabatabaei, P. Bouvry, and Y. Pigné. Generation of realistic mobility for vanets using genetic algorithms. In IEEE Congress on Evolutionary Computation, pages 1--8. IEEE, 2012.Google Scholar
- S. Uppoor and M. Fiore. Large-scale urban vehicular mobility for networking research. In O. Altintas, W. Chen, and G. J. Heijenk, editors, VNC, pages 62--69. IEEE, 2011.Google Scholar
- F. Wilcoxon. Individual comparisons by ranking methods. Biometrics Bulletin, 1(6):80--83, 1945.Google ScholarCross Ref
Index Terms
- Vehicular mobility model optimization using cooperative coevolutionary genetic algorithms
Recommendations
Neural network crossover in genetic algorithms using genetic programming
AbstractThe use of genetic algorithms (GAs) to evolve neural network (NN) weights has risen in popularity in recent years, particularly when used together with gradient descent as a mutation operator. However, crossover operators are often omitted from ...
Using messy genetic algorithms for solving the winner determination problem
GECCO '10: Proceedings of the 12th annual conference companion on Genetic and evolutionary computationThe paper presents a study on the application of messy genetic algorithms for the winner determination problem in the combinatorial auction realm. Messy genetic algorithms operate explicitly on building blocks in order to obtain good solutions. Since ...
Biased random-key genetic algorithms for combinatorial optimization
Random-key genetic algorithms were introduced by Bean (ORSA J. Comput. 6:154---160, 1994) for solving sequencing problems in combinatorial optimization. Since then, they have been extended to handle a wide class of combinatorial optimization problems. ...
Comments