MANET link performance using Ant Colony Optimization and Particle Swarm Optimization algorithms

End-to-end delay and Communication cost are the most important metrics in MANET (Mobile Adhoc Network) routing from source to destination. Recent approaches in Swarm intelligence (SI) technique, a local interaction of many simple agents to meet a global goal, prove that it has more impact on routing in MANETs. Ant Colony Optimization (ACO) algorithm uses mobile agents as ants to discover feasible and best path in a network. ACO helps in finding the paths between two nodes in a network and acts as an input to the Particle Swarm Optimization (PSO) technique, a metaheuristic approach in SI. PSO finds the best solution over the particle's position and velocity with the objective of cost and minimum End-to-end delay. This hybrid algorithm exhibits better performances when compared to ACO approach.

[1]  Andrew T. Campbell,et al.  INSIGNIA: An IP-Based Quality of Service Framework for Mobile ad Hoc Networks , 2000, J. Parallel Distributed Comput..

[2]  Mauro Fonseca,et al.  Routing and quality of service support for mobile ad hoc networks , 2007, Comput. Networks.

[3]  Zulfiqar Ali,et al.  Critical analysis of swarm intelligence based routing protocols in adhoc and sensor wireless networks , 2011, International Conference on Computer Networks and Information Technology.

[4]  Rehan Ashraf,et al.  Gossip Based Routing Protocol Design for Ad Hoc Networks , 2012 .

[5]  Sachin Kumar Gupta,et al.  Routing Protocols in Mobile Ad-hoc Networks \ , 2011 .

[6]  H. Bakht.  Routing protocols for mobile ad hoc networks , 2005 .

[7]  Qian Zhang,et al.  Shortest path routing in partially connected ad hoc networks , 2003, GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489).

[8]  Shayesteh Tabatabaei,et al.  Routing and quality of service support for mobile Ad hoc networks , 2010, 2010 2nd International Conference on Computer Engineering and Technology.

[9]  S. Sam,et al.  Survey on routing protocols on mobile adhoc networks , 2013, 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s).

[10]  K. Thanushkodi,et al.  Hybrid intelligent algorithm [improved particle swarm optimization (PSO) with ant colony optimization (ACO)] for multiprocessor job scheduling , 2012 .

[11]  Imed Bouazizi,et al.  ARA-the ant-colony based routing algorithm for MANETs , 2002, Proceedings. International Conference on Parallel Processing Workshop.

[12]  David A. Maltz,et al.  DSR: the dynamic source routing protocol for multihop wireless ad hoc networks , 2001 .