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Parameter Adaptation in Ant Colony Optimization

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Autonomous Search

Abstract

This chapter reviews the approaches that have been studied for the online adaptation of the parameters of ant colony optimization (ACO) algorithms, that is, the variation of parameter settings while solving an instance of a problem. We classify these approaches according to the main classes of online parameter-adaptation techniques. One conclusion of this review is that the available approaches do not exploit an in-depth understanding of the effect of individual parameters on the behavior of ACO algorithms. Therefore, this chapter also presents results of an empirical study of the solution quality over computation time for Ant Colony System and MAX-MIN Ant System, two well-known ACO algorithms. The first part of this study provides insights on the behaviour of the algorithms in dependence of fixed parameter settings. One conclusion is that the best fixed parameter settings of MAX-MIN Ant System depend strongly on the available computation time. The second part of the study uses these insights to propose simple, pre-scheduled parameter variations. Our experimental results show that such pre-scheduled parameter variations can dramatically improve the anytime performance of MAX-MIN Ant System.

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Authors and Affiliations

  1. IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium

    Thomas Stützle

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  1. Thomas Stützle
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  2. Manuel López-Ibáñez
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  3. Paola Pellegrini
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  4. Michael Maur
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  5. Marco Montes de Oca
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  6. Mauro Birattari
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  7. Marco Dorigo
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Correspondence to Thomas Stützle .

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Editors and Affiliations

  1. Microsoft Research Cambridge, JJ Thomson Avenue 7, Cambridge, CB3 0FB, United Kingdom

    Youssef Hamadi

  2. Federico Santa María, Departamento de Informática, Universidad Técnica, Avenida España 1680, Valparaíso, Chile

    Eric Monfroy

  3. Faculté des Sciences, LERIA, Université d'Angers, Boulevard Lavoisier 2, Angers CX 01, 49045, France

    Frédéric Saubion

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Stützle, T. et al. (2011). Parameter Adaptation in Ant Colony Optimization. In: Hamadi, Y., Monfroy, E., Saubion, F. (eds) Autonomous Search. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21434-9_8

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  • DOI: https://doi.org/10.1007/978-3-642-21434-9_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21433-2

  • Online ISBN: 978-3-642-21434-9

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