AntNet: Distributed Stigmergetic Control for Communications Networks

TL;DR

AntNet is a distributed, stigmergy-based routing algorithm inspired by ant colony behavior, demonstrating superior adaptive performance in various network conditions compared to existing methods.

Contribution

Introduces AntNet, a novel stigmergic, agent-based routing algorithm that improves adaptive network routing through decentralized exploration and indirect communication.

Findings

AntNet outperforms six state-of-the-art routing algorithms.

The algorithm adapts effectively to different network sizes and traffic patterns.

Results show significant improvements in routing efficiency and robustness.

Abstract

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.