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

Abstract

TThis paper presents a hybrid method that combines the genetic algorithm (GA) and the ant colony system algorithm (ACS), namely GACS, to solve the traffic routing problem. In the proposed framework, we use the genetic algorithm to optimize the ACS parameters in order to attain the best trips and travelling time through several novel functions to help ants to update the global and local pheromones. The GACS framework is implemented using the VANETsim package and the real city maps from the open street map project. The experimental results show that our framework achieves a considerably higher performance than A-Star and the classical ACS algorithms in terms of the length of the global best path and the time for trips. Moreover, the GACS framework is also efficient in solving the congestion problem by online monitoring the conditions of traffic light systems. KeywordsTraffic routing; Ant colony system; Genetic algorithm; VANET simulator. References [1] M. Dorigo, Ant colony optimization, Scholarpedia 2(3) (2007) 1461. https://doi.org/10/4249/scholarpedia.1461.[2] M.V. Dorigo, Maniezzo, A. Colorni, Ant system: Optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 26(1) (1996) 29-41.[3] M. Dorigo, L.M. Gambardella, Ant colony system: A cooperative learning approach to the traveling salesman problem, IEEE Transactions on evolutionary computation 1(1) (1997) 53-66.[4] M. Dorigo, T. St¨utzle, Ant Colony Optimization. MIT Press, Cambridge, MA, 2004.[5] D. Favaretto, E. Moretti, P. Pellegrini, On the explorative behavior of MAX-MIN Ant. System, In: St¨utzle T, Birattari M, Hoos HH (eds) Engineering Stochastic Local Search Algorithms, Designing, Implementing and Analyzing Effective Heuristics. SLS 2009, LNCS, Springer, Heidelberg, Germany 5752 (2009) 115-119.[6] F. Lobo, C.F. Lima, Z. Michalewicz (eds), Parameter Setting in Evolutionary Algorithms, Springer, Berlin, Germany, 2007.[7] T. Stützle, Manuel López-Ibáñez, Paola Pellegrini, Michael Maur, Marco Montes de Oca, Mauro Birattari, Marco Dorigo, Parameter adaptation in ant colony optimization in Autonomous search, Springer, 2011, pp. 191-215.[8] Z. Cai, H. Huang, Ant colony optimization algorithm based on adaptive weight and volatility parameters in Intelligent Information Technology Application, 2008. IITA'08, Second International Symposium IEEE, 2008.[9] J. Liu, Shenghua Xu, Fuhao Zhang, Liang Wang, A hybrid genetic-ant colony optimization algorithm for the optimal path selection, Intelligent Automation & Soft Computing, 2016, pp. 1-8.[10] D.Gaertner K.L. Clark, On Optimal Parameters for Ant Colony Optimization Algorithms, In IC-AI, 2005.[11] X.Wei, Parameters Analysis for Basic Ant Colony Optimization Algorithm in TSP, Reason 7(4) (2014) 159-170.[12] J.H. Holland, Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control and artificial intelligence, U Michigan Press, 1975.[13] K.D.E. Sastry, Goldberg, G. Kendall, Genetic algorithms, In Search methodologies, Springer, 2014, pp. 93-117.[14] S.M. Odeh, Management of an intelligent traffic light system by using genetic algorithm, Journal of Image and Graphics 1(2) (2013) 90-93.[15] A.M. Turky, M.S. Ahmad, M.Z.M. Yusoff, B.T. Hammad, Using Genetic Algorithm for Traffic Light Control System with a Pedestrian Crossing,  In: Wen P., Li Y., Polkowski L., Yao Y., Tsumoto S., Wang G. (eds) Rough Sets and Knowledge Technology, RSKT 2009, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg 5589 (2009) 512-519.[16] Y.R.B. Al-Mayouf, Mahamod Ismail1, Nor Fadzilah Abdullah, Salih M. Al-Qaraawi, Omar Adil Mahdi, Survey On Vanet Technologies And Simulation Models, 2006.[17] S.A. Ben Mussa, M. Manaf, K.Z. Ghafoor, Z. Doukha, "Simulation tools for vehicular ad hoc networks: A comparison study and future perspectives", 2015 International Conference on Wireless Networks and Mobile Communications (WINCOM), Marrakech, 2015, pp. 1-8.[18] V. Cristea, Victor Gradinescu, Cristian Gorgorin, Raluca Diaconescu, Liviu Iftode, Simulation of vanet applications, Automotive Informatics and Communicative Systems, 2009.[19] L. Liang, J. Ye, D. Wei, Application of improved ant colony system algorithm in optimization of irregular parts nesting, In 2008 Fourth International Conference on Natural Computation, IEEE, 2008.[20] X. Yan, Research on the Hybrid ant Colony Algorithm based on Genetic Algorithm. International Journal of Signal Processing, Image Processing and Pattern Recognition 9(3) (2016) 155-166.