Abstract
QoS-aware multicast routing service is becoming an important requirement of computer networks supporting group-based applications, such as multimedia conferencing, video conferencing, video telephony and distance learning. These real-time multimedia applications require the transmission of messages from a sender to multiple receivers subject to QoS constraints. This requires the underlying multicast routing protocol to find a QoS constrained minimum cost multicast spanning tree. However, the problem of finding the minimum cost multicast tree is known to be an NP -complete problem. In this paper, we present a new method GAACO to solve this minimum cost multicast routing problem. In this method, genetic algorithm (GA) and ant colony optimization (ACO) are combined to improve the computing performance. The simulation results show that the proposed GAACO algorithm has superior performance when compared to other existing algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, p. 432. Addison Wesley (1989)
Forrest, S., Mitchell, M.: Relative building-block fitness and the building-block hypothesis. In: Whitley, L.D. (ed.) Foundations of Genetic Algorithms 2. Morgan Kauffman, San Mateo (1993)
Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: Proceedings of ECAL 1991-European Conference on Artificial Life, pp. 134–142 (1991)
Colorni, A., Dorigo, M., Maniezzo, V.: An investigation of some properties of an ant algorithm. In: Proceedings of the Parallel Problem Solving from Nature Conference, pp. 509–520 (1992)
Younes, A.: An Ant Algorithm for Solving QoS Multicast Routing Problem. International Journal of Computer Science and Security (IJCSS) 5(1), 156–167 (2011)
Bullnheimer, B., Hartl, R.F., Strauss, C.: A new rank-based version of the ant system: a computational study. Central European Journal of Operations Research 7(1), 25–38 (1999)
Stützle, T., Hoos, H.: MAX-MIN Ant System and Local Search for the Traveling Salesman Problem. In: Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC 1997), pp. 309–314 (1997)
Wang, X.H., Wang, G.X.: A multicast routing approach with delay-constrained minimum-cost based on genetic algorithm. Journal of China Institute of Communications 23(3), 112–117 (2002)
Salama, H.F., Reeves, D.S., Viniotis, Y.: Evaluation of multicast routing algorithms for real-time communication on high-speed networks. IEEE Journal on Selected Areas in Communications (1997) 15(3), 332–345 (1997)
Peng, B., Li, L.: A Method for QoS Multicast Routing Based on Genetic Simulated Annealing Algorithm. International Journal of Future Generation Communication and Networking 5(1), 43–60 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Peng, B., Li, L. (2014). Combination of Genetic Algorithm and Ant Colony Optimization for QoS Multicast Routing. In: Cho, Y., Matson, E. (eds) Soft Computing in Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 270. Springer, Cham. https://doi.org/10.1007/978-3-319-05515-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-05515-2_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05514-5
Online ISBN: 978-3-319-05515-2
eBook Packages: EngineeringEngineering (R0)