Ant Algorithms for Discrete Optimization

1999/04/01 by Marco Dorigo, Gianni A. Di, Gianni Di Caro +2 · 12 citations
Engineering · #Optimization and Packing Problems #Scheduling and Optimization Algorithms #Vehicle Routing Optimization Methods

paper · doi:10.1162/106454699568728

openalex publication_date 1999/04/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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 ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.

Citations

• Self-organized shortcuts in the Argentine ant
• A Method for Solving Traveling-Salesman Problems

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