Abstract

Mobile ad hoc networks are infrastructure-less networks consisting of wireless, possibly mobile nodes which are organized in peer-to-peer and autonomous fashion. Each node is also a router that forwards data packets to its proper destination. A new family of algorithms inspired by Swarm Intelligence has come into existence to provide route optimization through routing load distribution. In this paper, we have proposed an Ant based routing algorithm to ensure appropriate load balancing in mobile ad hoc network using AODV and Ant Colony Optimization metaheuristics.

References

• A. Ajith, G. Crina and R. Vitorino 2006, Stigmergic Optimization, Studies in Computational Intelligence, vol. 31, 299.
• Marco Dorigo, Mauro Birattari, and Thomas Scutzle 2006, Ant Colony Optimization Artificial Ants as a Computational Intelligence Technique, IRIDIA - Technical Report Series Technical Report No. TR/IRIDIA/2006-023.
• J.Deneubourg, S. Aron, S. Goss, and J. Pasteels 1990, The self organizing exploratory pattern of the Argentine ant, Journal of Insect Behavior, vol. 3.
• S. Goss, S. Aron, J.-L. Deneubourg, and J. M. Pasteels 1989, Self-organized shortcuts in the Argentine ant, Naturwissenschaften, vol. 76.
• M. Dorigo, V. Maniezzo, and A Colomi 1996, Ant System: Optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man, and Cybernetics , vol. 26(1), 29-41,
• M. Dorigo and T. SCutzle 2004, Ant Colony Optimization, MIT Press, Cambridge, MA.
• M. Dorigo, G. Di Caro, and L. M. Gambardella 1999, Ant algorithms fordiscrete optimization, Artificial Life, vol. 5(2), 137-172.
• V. Cerny 1985, A thermo dynamical approach to the traveling salesman problem, Journal of Optimization Theory and Applications, vol. 45(1), 41-51.
• F. Glover and M. Laguna 1997, Tabu Search, Kluwer Academic Publishers.
• H. R Lourenc, O. Martin, and T. Srutzle 2002, Iterated local search, Handbook of Metaheuristics, International Series in Operations Research & Management Science, F. Glover and G. Kochenberger, Eds. Kluwer Academic Publishers, vol. 57, 321-353.
• S.S. Dhillon, P. Van Mieghem 2007, Performance analysis of the AntNet algorithm, Computer Networks, vol. 51,2104-2125.
• Gianni Di Caro and Marco Dorigo 1998, AntNet: distributed stigmergetic control for communication networks, Journal of Artificial Intelligence Research voI.9, 317-365.
• Ruud Schoonder woerd, Owen Holland, Janet Broten 1997, Ant like agents for load balancing in telecommunication networks, Proceedings of the First International Conference on Autonomous agents.
• M. Heusse, D. Snyers, S. Guerin and P. Kuntz 1998, Adaptive agent-driven routing and load balancing in communication networks, Rapport technique de l'ENST de Brestagne, RR-98001-iasc.
• S. Rajagopalan and C. Shen 2005, ANSI: a unicast routing protocol for mobile ad hoc networks using swarm intelligence, Proceedings of the International Conference on Artificial Intelligence, 24-27.
• D. Camara, and A. Loureiro 2000, A novel routing algorithm for ad hoc networks, Proceedings of 33rd Annual Hawaii International Conference on System Sciences,1-8.
• T. White 1997, "Swarm intelligence and problem tele communications", Canadian Artificial Magazine ,Spring.
• Kawabata, K.; Suzuki, T.; Hayama, T.; Kobayashi, H. 1996, Distributed intelligent control structure for multi-legged walking robots, Proceeding of 4th International Workshop on Advanced Motion Control, (AMC '96-MIE), vol. 1, 231 -236.
• A. Colorni, M. Dorigo, and V. Maniezzo 1992, "An investigation of some properties of an ant algorithm", Proceeding of Parallel Problem Solving from Nature Conference (PPSN 92), Brussels, Belgium, 509-520.
• Manpreet Singh 2010, GRAAA: Grid Resource Allocation Based on Ant Algorithm, Journal of Advances in Information Technology, vol. 1(3), 133-135.