Combination of genetic algorithm and ant colony optimization for qos multicast routing

被引：0

作者：

Peng, Bo ^{[1
]}

Li, Lei ^{[2
]}

机构：

[1] Graduate School of Engineering, Hosei University, Koganei, 184-8584, Tokyo

[2] Hosei University, Koganei, 184-8584, Tokyo

来源：

Advances in Intelligent Systems and Computing | 2014年 / 270卷

关键词：

Ant Colony Optimization; Genetic Algorithm; Multicast Communication; Multicast Routing; QoS;

D O I：

10.1007/978-3-319-05515-2_6

中图分类号：

学科分类号：

摘要：

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. © Springer International Publishing Switzerland 2014.

引用

页码：49 / 56

页数：7

共 11 条

[1]

Holland J.H., Adaptation in Natural and Artificial Systems, (1975)

[2]

Goldberg D.E., Genetic Algorithms in Search, Optimization and Machine Learning, (1989)

[3]

Forrest S., Mitchell M., Relative building-block fitness and the building-block hypothesis, Foundations of Genetic Algorithms, (1993)

[4]

Colorni A., Dorigo M., Maniezzo V., Distributed optimization by ant colonies, Proceedings of ECAL 1991-European Conference on Artificial Life, pp. 134-142, (1991)

[5]

Colorni A., Dorigo M., Maniezzo V., An investigation of some properties of an ant algorithm, Proceedings of the Parallel Problem Solving from Nature Conference, pp. 509-520, (1992)

[6]

Younes A., An Ant Algorithm for Solving QoS Multicast Routing Problem, International Journal of Computer Science and Security (IJCSS), 5, 1, pp. 156-167, (2011)

[7]

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, pp. 25-38, (1999)

[8]

Stutzle T., Hoos H., MAX-MIN Ant System and Local Search for the Traveling Salesman Problem, Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC 1997), pp. 309-314, (1997)

[9]

Wang X.H., Wang G.X., A multicast routing approach with delay-constrained minimumcost based on genetic algorithm, Journal of China Institute of Communications, 23, 3, pp. 112-117, (2002)

[10]

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, pp. 332-345, (1997)

← 1 2 →