research-article

Modeling ant colony foraging in dynamic and confined environment

Authors:

Elton Bernardo Bandeira de Melo,

Aluízio Fausto Ribeiro AraújoAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 169 - 176

https://doi.org/10.1145/1389095.1389124

Published: 12 July 2008 Publication History

Abstract

The collective foraging behavior of ants is an example of self-organization and

adaptation arising from the superposition of simple individual behavior. With the objective of understanding and modeling such interactions, experiments with the Argentine ants Linepithema humile were conducted into a relatively complex, artificial network. This consisted of interconnected branches and bifurcations, where the ants have to choose among fourteen different paths in order to reach a food source, and the branches can be blocked or unblocked at any time. Due mainly to stagnation problems, previous models did not accurately reproduce the behavior of ants in a changing environment. In this paper, a new model (ACF-DCM) is proposed, based on ACO principles and biological studies of insects. ACF-DCM succeeded in reproducing the behavior of ants in a confined and dynamic environment.

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Cited By

Jimenez Romero CYegenoglu APérez Martín ADiaz-Pier SMorrison A(2023)Emergent communication enhances foraging behavior in evolved swarms controlled by spiking neural networksSwarm Intelligence10.1007/s11721-023-00231-618:1(1-29)Online publication date: 14-Dec-2023
https://doi.org/10.1007/s11721-023-00231-6
Ramsch KReid CBeekman MMiddendorf M(2012)A mathematical model of foraging in a dynamic environment by trail-laying Argentine antsJournal of Theoretical Biology10.1016/j.jtbi.2012.04.003306(32-45)Online publication date: Aug-2012
https://doi.org/10.1016/j.jtbi.2012.04.003

Index Terms

Modeling ant colony foraging in dynamic and confined environment
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Multi-agent systems
  2. Modeling and simulation
    1. Model development and analysis

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cover image ACM Conferences

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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Cited By

Jimenez Romero CYegenoglu APérez Martín ADiaz-Pier SMorrison A(2023)Emergent communication enhances foraging behavior in evolved swarms controlled by spiking neural networksSwarm Intelligence10.1007/s11721-023-00231-618:1(1-29)Online publication date: 14-Dec-2023
https://doi.org/10.1007/s11721-023-00231-6
Ramsch KReid CBeekman MMiddendorf M(2012)A mathematical model of foraging in a dynamic environment by trail-laying Argentine antsJournal of Theoretical Biology10.1016/j.jtbi.2012.04.003306(32-45)Online publication date: Aug-2012
https://doi.org/10.1016/j.jtbi.2012.04.003

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