Ant Colony Optimization: Overview and Recent Advances

Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 146)

Abstract

Ant Colony Optimization (ACO) is a metaheuristic that is inspired by the pheromone trail laying and following behavior of some ant species. Artificial ants in ACO are stochastic solution construction procedures that build candidate solutions for the problem instance under concern by exploiting (artificial) pheromone information that is adapted based on the ants’ search experience and possibly available heuristic information. Since the proposal of the Ant System, the first ACO algorithm, many significant research results have been obtained. These contributions focused on the development of high-performing algorithmic variants, the development of a generic algorithmic framework for ACO algorithms, successful applications of ACO algorithms to a wide range of computationally hard problems, and the theoretical understanding of properties of ACO algorithms. This chapter reviews these developments and gives an overview of recent research trends in ACO.

Keywords

Migration Transportation Metaphor 

Notes

Acknowledgments

This work was supported by the META-X project, an Action de Recherche Concertée funded by the Scientific Research Directorate of the French Community of Belgium. Marco Dorigo and Thomas Stützle acknowledge support from the Belgian F.R.S.-FNRS, of which they are a Research Director and a Research Associate, respectively.

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Authors and Affiliations

  1. 1.IRIDIA, Université Libre de Bruxelles (ULB)BrusselsBelgium

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