AntNet: Distributed Stigmergetic Control for Communications Networks

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 algo rithms coming from the telecommunications and machine learning elds The algorithms performance is evaluated over a set of realistic testbeds We run many experiments over real and arti cial IP datagram networks with increasing number of nodes and under sev eral paradigmatic spatial and temporal tra c 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