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Ant Colony Optimization: Overview and Recent Advances

In: Handbook of Metaheuristics

Author

Listed:
  • Marco Dorigo

    (IRIDIA, Université Libre de Bruxelles (ULB))

  • Thomas Stützle

    (IRIDIA, Université Libre de Bruxelles (ULB))

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.

Suggested Citation

  • Marco Dorigo & Thomas Stützle, 2010. "Ant Colony Optimization: Overview and Recent Advances," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 227-263, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-1665-5_8
    DOI: 10.1007/978-1-4419-1665-5_8
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    Citations

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    Cited by:

    1. Zühal Kartal & Mohan Krishnamoorthy & Andreas T. Ernst, 2019. "Heuristic algorithms for the single allocation p-hub center problem with routing considerations," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(1), pages 99-145, March.
    2. Nebojsa Bacanin & Timea Bezdan & Eva Tuba & Ivana Strumberger & Milan Tuba, 2020. "Monarch Butterfly Optimization Based Convolutional Neural Network Design," Mathematics, MDPI, vol. 8(6), pages 1-33, June.
    3. Kucukkoc, Ibrahim & Li, Zixiang & Karaoglan, Aslan D. & Zhang, David Z., 2018. "Balancing of mixed-model two-sided assembly lines with underground workstations: A mathematical model and ant colony optimization algorithm," International Journal of Production Economics, Elsevier, vol. 205(C), pages 228-243.

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