Antnet is a software agent-based routing algorithm that is influenced by the unsophisticated and individual ant’s emergent behaviour. The aim of this chapter is twofold, firstly to introduce improvements to the antnet routing algorithm and then to critically review the work that is done around antnet and reinforcement learning in routing applications. In this chapter a modified antnet algorithm for packet-based networks has been proposed, which offers improvement in the throughput and the average delay by detecting and dropping packets routed through the non-optimal routes. The effect of traffic fluctuations has been limited by applying boundaries to the reinforcement parameter. The round trip feedback information supplied by the software agents is reinforced by updated probability entries in the distance vector table. In addition, link usage information is also used to prevent stagnation problems. Also discussed is antnet with multiple ant colonies applied to packet switched networks. Simulation results show that the average delay experienced by data packets is reduced for evaporation for all cases when non-uniform traffic model traffic is used. However, there is no performance gain on the uniform traffic models. In addition, multiple ant colonies are applied to the packet switched networks, and results are compared with the other approaches. Results show that the throughput could be increased when compared to other schemes, but with no gain in the average packet delay time.
|Title of host publication||Intelligent Systems for Optical Networks Design: Advancing Techniques|
|Editors||Firat Kavian, Zabih Ghassemlooy|
|Place of Publication||Hershey, PA|
|Publication status||Published - Mar 2013|