research-article

Synergy in ant foraging strategies: memory and communication alone and in combination

Authors:

Kenneth Letendre,

Melanie E. MosesAuthors Info & Claims

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

Pages 41 - 48

https://doi.org/10.1145/2463372.2463389

Published: 06 July 2013 Publication History

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

Synergy in ant foraging strategies: memory and communication alone and in combination

Pages 41 - 48

Abstract
References

Abstract

Collective foraging is a canonical problem in the study of social insect behavior, as well as in biologically inspired engineered systems. Pheromone recruitment is a well-studied mechanism by which ants coordinate their foraging. Another mechanism for information use is the memory of individual ants, which allows an ant to return to a site it has previously visited. There is synergy in the use of social and private information: ants with poor private information can follow pheromone trails; while ants with private information can ignore trails and instead rely on memory. We developed an agent-based model of foraging by harvester ants, and optimized the model to maximize foraging rate using genetic algorithms. We found that ants' individual memory provided greater benefit in terms of increased foraging rate than pheromone trails in a variety of food distributions. When the two strategies are used together, they out-perform either strategy alone. We compare the behavior of these models to observations of harvester ants in the field. We discuss why individual memory is more beneficial in this system than pheromone trails. We suggest that individual memory may be an important addition to ant colony optimization and swarm robotics systems, and that genetic algorithms may be useful in finding an adaptive balance between individual foraging based on memory and recruitment based on communication.

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Cited By

EL BOUKHRISSI ATAHERI ABENNAS NREYES-LÓPEZ J(2023)Efficiency of foraging behavior in the ant genus Messor (Hymenoptera: Formicidae: Myrmicinae) in response to food distributionEuropean Journal of Entomology10.14411/eje.2023.039120(357-365)Online publication date: 12-Dec-2023
https://doi.org/10.14411/eje.2023.039
Muratore IGarnier S(2023)Ontogeny of collective behaviourPhilosophical Transactions of the Royal Society B: Biological Sciences10.1098/rstb.2022.0065378:1874Online publication date: 20-Feb-2023
https://doi.org/10.1098/rstb.2022.0065
Levin M(2023)Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biologyCellular and Molecular Life Sciences10.1007/s00018-023-04790-z80:6Online publication date: 8-May-2023
https://doi.org/10.1007/s00018-023-04790-z
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Index Terms

Synergy in ant foraging strategies: memory and communication alone and in combination
1. Computing methodologies
  1. Artificial intelligence
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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cover image ACM Conferences

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

July 2013

1672 pages

ISBN:9781450319638

DOI:10.1145/2463372

Editor:
Christian Blum
IKERBASQUE and University of the Basque Country UPV/EHU, Spain
,
General Chair:
Enrique Alba
University of Malaga, Spain

Copyright © 2013 ACM.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Published: 06 July 2013

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GECCO '13: Genetic and Evolutionary Computation Conference

July 6 - 10, 2013

Amsterdam, The Netherlands

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GECCO '13 Paper Acceptance Rate 204 of 570 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

EL BOUKHRISSI ATAHERI ABENNAS NREYES-LÓPEZ J(2023)Efficiency of foraging behavior in the ant genus Messor (Hymenoptera: Formicidae: Myrmicinae) in response to food distributionEuropean Journal of Entomology10.14411/eje.2023.039120(357-365)Online publication date: 12-Dec-2023
https://doi.org/10.14411/eje.2023.039
Muratore IGarnier S(2023)Ontogeny of collective behaviourPhilosophical Transactions of the Royal Society B: Biological Sciences10.1098/rstb.2022.0065378:1874Online publication date: 20-Feb-2023
https://doi.org/10.1098/rstb.2022.0065
Levin M(2023)Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biologyCellular and Molecular Life Sciences10.1007/s00018-023-04790-z80:6Online publication date: 8-May-2023
https://doi.org/10.1007/s00018-023-04790-z
Nguyen TBanerjee B(2021)Reinforcement learning as a rehearsal for swarm foragingSwarm Intelligence10.1007/s11721-021-00203-8Online publication date: 29-Sep-2021
https://doi.org/10.1007/s11721-021-00203-8
García-Meza DAndresen ERíos-Casanova LMartorell C(2021)Seed density in monospecific and mixed patches affects individual and collective foraging in antsInsectes Sociaux10.1007/s00040-020-00800-668:1(81-92)Online publication date: 2-Jan-2021
https://doi.org/10.1007/s00040-020-00800-6
Isaacs JDolan-Stern NGetzinger MWarner EVenegas ASanchez A(2020)Central Place Foraging: Delivery Lanes, Recruitment and Site Fidelity2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)10.1109/ICARSC49921.2020.9096070(319-324)Online publication date: Apr-2020
https://doi.org/10.1109/ICARSC49921.2020.9096070
Moses MCannon JGordon DForrest S(2019)Distributed Adaptive Search in T Cells: Lessons From AntsFrontiers in Immunology10.3389/fimmu.2019.0135710Online publication date: 13-Jun-2019
https://doi.org/10.3389/fimmu.2019.01357
Aggarwal AGupta DVining WFricke GMoses M(2019)Ignorance is Not Bliss: An Analysis of Central-Place Foraging Algorithms2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS40897.2019.8967665(6510-6517)Online publication date: Nov-2019
https://doi.org/10.1109/IROS40897.2019.8967665
Flanagan TBeyeler WLevin DFinley PMoses M(2019)Movement and Spatial Specificity Support Scaling in Ant Colonies and Immune Systems: Application to National BiosurveillanceEvolution, Development and Complexity10.1007/978-3-030-00075-2_15(355-366)Online publication date: 26-Jun-2019
https://doi.org/10.1007/978-3-030-00075-2_15
Vaughan N(2018)Swarm communication by evolutionary algorithms2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)10.1109/EAIS.2018.8397189(1-9)Online publication date: May-2018
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