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The Ant Colony Optimization Meta-Heuristic

November 1999

November 1999

Authors:

Marco Dorigo

Université Libre de Bruxelles

Gianni A. Di Caro

Carnegie Mellon University

Introduction Ant algorithms are multi-agent systems in which the behavior of each single agent, called arti cial ant or ant for short in the following, is inspired by the behavior of real ants. Ant algorithms are one of the most successful examples of swarm intelligent systems [3], and have been applied to many types of problems, ranging from the classical traveling salesman problem, to routing in telecommunications networks. In this section we will focus on the ant colony optimization (ACO) meta-heuristic [18], which de nes a particular class of ant algorithms, called in the following ACO algorithms. ACO algorithms have been inspired by the following experience run by Goss et al. [31] using a colony of real ants. A laboratory colony of Argentine ants (Iridomyrmex humilis) is given access to a food source in an arena linked to the colony's nest by a bridge with two branches of dierent length (see gure 2.1). Branches are arranged in such a way that ants going in either direction (

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