  This paper introduces AntNet, a novel approach to the adaptive learning of
routing tables in communications networks. AntNet is a distributed, mobile
agents based Monte Carlo system that was inspired by recent work on the ant
colony metaphor for solving optimization problems. AntNet's agents concurrently
explore the network and exchange collected information. The communication among
the agents is indirect and asynchronous, mediated by the network itself. This
form of communication is typical of social insects and is called stigmergy. We
compare our algorithm with six state-of-the-art routing algorithms coming from
the telecommunications and machine learning fields. The algorithms' performance
is evaluated over a set of realistic testbeds. We run many experiments over
real and artificial IP datagram networks with increasing number of nodes and
under several paradigmatic spatial and temporal traffic distributions. Results
are very encouraging. AntNet showed superior performance under all the
experimental conditions with respect to its competitors. We analyze the main
characteristics of the algorithm and try to explain the reasons for its
superiority.
