IDEAS home Printed from https://ideas.repec.org/p/por/fepwps/474.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: http://www.fep.up.pt/investigacao/workingpapers/wp474.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vitayasak, Srisatja & Pongcharoen, Pupong & Hicks, Chris, 2017. "A tool for solving stochastic dynamic facility layout problems with stochastic demand using either a Genetic Algorithm or modified Backtracking Search Algorithm," International Journal of Production Economics, Elsevier, vol. 190(C), pages 146-157.
    2. Sachuer Bao & Chi Zhang & Min Ouyang & Lixin Miao, 2019. "An integrated tri-level model for enhancing the resilience of facilities against intentional attacks," Annals of Operations Research, Springer, vol. 283(1), pages 87-117, December.
    3. Yu-Hsin Chen, Gary, 2013. "A new data structure of solution representation in hybrid ant colony optimization for large dynamic facility layout problems," International Journal of Production Economics, Elsevier, vol. 142(2), pages 362-371.
    4. Gintaras Palubeckis & Armantas Ostreika & Jūratė Platužienė, 2022. "A Variable Neighborhood Search Approach for the Dynamic Single Row Facility Layout Problem," Mathematics, MDPI, vol. 10(13), pages 1-27, June.
    5. Balakrishnan, Jaydeep & Hung Cheng, Chun, 2009. "The dynamic plant layout problem: Incorporating rolling horizons and forecast uncertainty," Omega, Elsevier, vol. 37(1), pages 165-177, February.
    6. Wu, Desheng & Olson, David L. & Wang, Shouyang, 2019. "Finance-operations interface mechanism and models," Omega, Elsevier, vol. 88(C), pages 1-3.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:por:fepwps:474. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/fepuppt.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.