A Swarm Intelligence Based Algorithm for QoS Multicast Routing Problem
The QoS multicast routing problem is to find a multicast routing tree with minimal cost that can satisfy constraints such as bandwidth, delay, delay jitter and loss rate. This problem is NP Complete. In this paper, we present a swarming agent based intelligence algorithm using a hybrid Ant Colony Optimization/Particle Swarm Optimization (ACO/PSO) algorithm to optimize the multicast tree. The algorithm starts with generating a large amount of mobile agents in the search space. The ACO algorithm guides agents’ movement by pheromones in the shared environment locally and the global maximum of the attribute values are obtained through the random interaction between the agents using PSO algorithm. The performance of the proposed algorithm is evaluated through simulation. The simulation results reveal that our algorithm performs better than the existing algorithms.
KeywordsParticle Swarm Optimization Source Node Destination Node Particle Swarm Optimization Algorithm Mobile Agent
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- 2.Di Caro, G., Dorigo, M.: AntNet: a mobile agents for adaptive routing. In: Proceedings of the 31st Hawaii International Conference on Systems, pp. 74–83 (1998)Google Scholar
- 4.Dorigo, M., Di Caro, G.: The ant colony optimization meta-heuristic, new ideas in optimization. McGraw-Hill (1999)Google Scholar
- 10.Li, C., Cao, C., Li, Y., Yu, Y.: Hybrid of genetic algorithm and particle swarm optimization for multicast QoS routing. In: IEEE International Conference on Control and Automation, pp. 2355–2359 (2007)Google Scholar
- 11.Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: IEEE International Conference on Neural Network, Perth, Australia, pp. 1942–1948 (1995)Google Scholar
- 14.Brueckner, S.A., Parunak, H.V.D.: Swarming agents for distributed pattern detection and classification. In: AAMAS, Bologna, Italy, July 15-19 (2002)Google Scholar
- 15.Meng, Y.: A Swarm Intelligence Based Algorithm for Proteomic Pattern Detection of Ovarian Cancer. In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, Toronto, Canada, September 28-29 (2006)Google Scholar