文档介绍：An adaptive multi-agent routing algorithm
inspired by ants behavior
Gianni Di Caro and Marco Dorigo
IRIDIA – Université Libre de Bruxelles – Belgium
{gdicaro, mdorigo}***@
Abstract. This paper introduces , a novel adaptive approach to routing tables
learning in works. is inspired by the stigmergy
communication model observed in ant colonies. pare with the current In-
routing algorithm (OSPF), some old routing algorithms (SPF and distrib-
uted adaptive Bellman-Ford), and recently proposed forms of asynchronous online Bell-
man-Ford (Q-routing and Predictive Q-routing). In all the experimental conditions con-
sidered outperforms peting algorithms, where performance is measured
by standard measures such work throughput and average packet delay,
1. Introduction
Real ants are able to find shortest paths using as only information the pheromone trail
deposited by other ants [1]. Ant colony optimization (ACO) algorithms which take
inspiration from ants' behavior in finding shortest paths have recently been ess-
fully applied binatorial optimization [3,6,11,12,13]. In ant colony optimization
a set of artificial ants collectively solve binatorial problem by a cooperative ef-
fort. This effort is mediated by munication [3, 14], that is, a form of
munication of information on the problem structure ants collect while
building solutions.
In this paper we present , a novel ACO algorithm applied to the routing
problem in works. In artificial ants collec-
tively solve the routing problem by a cooperative effort in which stigmergy plays a
prominent role. Ants build local models of work status and adaptive routing ta-
bles using indirect and munication of information they collect
while exploring work.
pare on a variety of realistic experimental conditions with the fol-
lowing state-of-the-art routing algorithms: Open Shortest Path First (OSPF) [16],
Shortest Path First (SPF) [15], distributed adaptive Bellman-Ford [18], and to s