DOI QR코드

DOI QR Code

A Hybrid Method Based on Genetic Algorithm and Ant Colony System for Traffic Routing Optimization

  • Thi-Hau Nguyen (University of Engineering and Technology, Vietnam National University) ;
  • Ha-Nam Nguyen (University of Engineering and Technology, Vietnam National University) ;
  • Dang-Nhac Lu (Academy of Journalism and Communication) ;
  • Duc-Nhan Nguyen (Posts and Telecommunications Institute of Technology)
  • Received : 2023.08.05
  • Published : 2023.08.30

Abstract

The Ant Colony System (ACS) is a variant of Ant colony optimization algorithm which is well-known in Traveling Salesman Problem. This paper proposed a hybrid method based on genetic algorithm (GA) and ant colony system (ACS), called GACS, to solve traffic routing problem. In the GACS, we use genetic algorithm to optimize the ACS parameters that aims to attain the shortest trips and time through new functions to help the ants to update global and local pheromones. Our experiments are performed by the GACS framework which is developed from VANETsim with the ability of real map loading from open street map project, and updating traffic light in real-time. The obtained results show that our framework acquired higher performance than A-Star and classical ACS algorithms in terms of length of the best global tour and the time for trip.

Keywords

Acknowledgement

This research was funded by Vietnam National University, Hanoi (VNU) under the project no. QG 17.39.

References

  1. Dorigo, M., V. Maniezzo, and A. Colorni, Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1996. 26(1): p. 29-41.  https://doi.org/10.1109/3477.484436
  2. Dorigo, M. and L.M. Gambardella, Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on evolutionary computation, 1997. 1(1): p. 53-66.  https://doi.org/10.1109/4235.585892
  3. Stutzle, T., et al., Parameter adaptation in ant colony optimization, in Autonomous search. 2011, Springer. p. 191-215. 
  4. Liu, J., et al., A hybrid genetic-ant colony optimization algorithm for the optimal path selection. Intelligent Automation & Soft Computing, 2016: p. 1-8. 
  5. Cai, Z. and H. Huang. Ant colony optimization algorithm based on adaptive weight and volatility parameters. in Intelligent Information Technology Application, 2008. IITA'08. Second International Symposium on. 2008. IEEE. 
  6. Gaertner, D. and K.L. Clark. On Optimal Parameters for Ant Colony Optimization Algorithms. in IC-AI. 2005. 
  7. Wei, X., Parameters Analysis for Basic Ant Colony Optimization Algorithm in TSP. reason, 2014. 7(4). 
  8. Holland, J.H., Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. 1975: U Michigan Press. 
  9. Sastry, K., D.E. Goldberg, and G. Kendall, Genetic algorithms, in Search methodologies. 2014, Springer. p. 93-117. 
  10. Odeh, S.M., Management of an intelligent traffic light system by using genetic algorithm. Journal of Image and Graphics, 2013. 1(2): p. 90-93.  https://doi.org/10.12720/joig.1.2.90-93
  11. Al-Mayouf, Y.R.B., et al., SURVEY ON VANET TECHNOLOGIES AND SIMULATION MODELS. 2006. 
  12. Mussa, S.A.B., et al. Simulation tools for vehicular ad hoc networks: A comparison study and future perspectives. in Wireless Networks and Mobile Communications (WINCOM), 2015 International Conference on. 2015. IEEE. 
  13. Cristea, V., et al., Simulation of vanet applications. Automotive Informatics and Communicative Systems, 2009. 
  14. Liang, L., J. Ye, and D. Wei. Application of improved ant colony system algorithm in optimization of irregular parts nesting. in 2008 Fourth International Conference on Natural Computation. 2008. IEEE. 
  15. Yan, X., Research on the Hybrid ant Colony Algorithm based on Genetic Algorithm. International Journal of Signal Processing, Image Processing and Pattern Recognition, 2016. 9(3): p. 155-166. https://doi.org/10.14257/ijsip.2016.9.3.14