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Ant Colony Optimization: Overview and Recent Advances

  • Marco Dorigo
  • Thomas Stützle
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 272)

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 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 algorithm, successful applications of ACO algorithms to a wide range of computationally hard problems, and the theoretical understanding of important properties of ACO algorithms. This chapter reviews these developments and gives an overview of recent research trends in ACO.

Notes

Acknowledgements

This work was supported by the COMEX project, P7/36, within the Interuniversity Attraction Poles Programme of the Belgian Science Policy Office. Marco Dorigo and Thomas Stützle acknowledge support from the Belgian F.R.S.-FNRS, of which they are Research Directors.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Marco Dorigo
    • 1
  • Thomas Stützle
    • 1
  1. 1.IRIDIAUniversité Libre de Bruxelles (ULB)BrusselsBelgium

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