TWO ANT COLONY ALGORITHMS FOR BEST-EFFORT ROUTING IN DATAGRAM NETWORKS GIANNI DI CARO and MARCO DORIGO

Mobile Agents for Adaptive Routing

This paper introduces AntNet, a new routing algorithm for telecommunication networks. AntNet is an adaptive, distributed, mobile-agents-based algorithm which was inspired by recent work on the ant colony metaphor. We apply AntNet in a datagram network and compare it with both static and adaptive state-ofthe-art routing algorithms. We ran experiments for various paradigmatic temporal and spatial...

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 an...

The Ant Colony Optimization Meta-Heuristic

Ant Algorithms for Discrete Optimization

This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the AC...

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 in...