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Ant Colony Optimization: a literature survey

Author

Listed:
  • Marta S.R. Monteiro

    (Faculdade de Economia da Universidade do Porto)

  • Dalila B.M.M. Fontes

    (Faculdade de Economia da Universidade do Porto)

  • Fernando A.C.C. Fontes

    (Faculdade de Engenharia da Universidade do Porto)

Abstract

Scientific literature is prolific both on exact and on heuristic solution methods developed to solve optimization problems. Although the former methods have an indisputable theoretical value when it comes to solve large realistic combinatorial optimization problems they are usually associated with large and even prohibitive running times. Heuristic methods, do not guarantee to determine a global optimal solution for a problem but are usually able to find a good solution rapidly, perhaps a local optimum, and require less computational resources. Ant Colony Optimization (ACO) algorithms belong to a class of heuristics based on the behaviour of nature ants. These algorithms have been used to solve many combinatorial optimization problems and have been known to outperform other popular heuristics such as Genetic Algorithms. Therefore, we believe that the number of ACO based algorithms will continue to grow for a long time. The contribution of this work is to provide the reader with a sort of consultation guide for developing ACO algorithms, by presenting a collection of different approaches that can be found in literature, regarding the ACO building blocks.

Suggested Citation

  • Marta S.R. Monteiro & Dalila B.M.M. Fontes & Fernando A.C.C. Fontes, 2012. "Ant Colony Optimization: a literature survey," FEP Working Papers 474, Universidade do Porto, Faculdade de Economia do Porto.
    • Handle: RePEc:por:fepwps:474
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    File URL: http://www.fep.up.pt/investigacao/workingpapers/wp474.pdf
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    References listed on IDEAS

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    1. Baykasoglu, Adil & Dereli, Turkay & Sabuncu, Ibrahim, 2006. "An ant colony algorithm for solving budget constrained and unconstrained dynamic facility layout problems," Omega, Elsevier, vol. 34(4), pages 385-396, August.
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    More about this item

    Keywords

    Ant Colony Optimization; Survey; Heuristics; Combinatorial Optimization Problems;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

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