Successful implementation and operation of a network largely depends on the routing algorithm in use. To date, several routing algorithms are in use but the problem with these algorithms is that they are either not adaptive or not robust enough, thus limiting the proper use of bandwidth. AntNet is an innovative algorithm that may be used for data networks. It is a combination of both static and dynamic routing algorithms. In this algorithm, a group of mobile agents (compared to real ants) form paths between source and destination nodes. They explore the network continuously and exchange obtained information indirectly, in order to update the routing tables at different nodes. Our version of AntNet (hereinafter referred to as AntNet2.0) has been improved to overcome the problems with other algorithms. This paper compares the performance of AntNet2.0 against two other commercially popular algorithms, viz. link state routing algorithm and distant vector routing algorithm. The performance matrix used to compare the algorithms is based on average throughput, packet loss, packet drop and end-to-end delay. Convergence time for this algorithm on a nation-wide telecommunications network will also be discussed. Conclusions and areas of further work will also be presented in lucid manner, so that it may be transformed into real practice in the future.Key Words: mobile agents, swarm intelligence, networks and constant bit rate
Minimax end-to-end delay routing and capacity assignment for virtual circuit networks