Two Ant Colony Algorithms for Best-Effort Routing in Datagram Networks

In this paper we present two versions of AntNet, a novel approach to adaptive learning of routing tables in wide area best-effort datagram networks. AntNet is a distributed multi-agent system inspired by the stigmergy model of communication observed in ant colonies. We report simulation results for AntNet on realistically sized networks using as performance measures throughput, packet delays and resources utilization. Our tests show that both instances of AntNet show superior performance with respect to the current Internet routing algorithm (OSPF), some improved old Internet routing algorithms (SPF and distributed adaptive Bellman-Ford), and recently proposed forms of asynchronous online Bellman-Ford (Q-routing and Predictive Q-routing).

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