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Inspiration for optimization from social insect behaviour

Author

Listed:
  • E. Bonabeau

    (Santa Fe Institute
    Eurobios, Tour Ernst & Young)

  • M. Dorigo

    (IRIDIA, Université Libre de Bruxelles)

  • G. Theraulaz

    (Laboratoire d’Ethologie et Cognition Animale, CNRS-FRE 2041, Université Paul Sabatier)

Abstract

Research in social insect behaviour has provided computer scientists with powerful methods for designing distributed control and optimization algorithms. These techniques are being applied successfully to a variety of scientific and engineering problems. In addition to achieving good performance on a wide spectrum of ‘static’ problems, such techniques tend to exhibit a high degree of flexibility and robustness in a dynamic environment.

Suggested Citation

  • E. Bonabeau & M. Dorigo & G. Theraulaz, 2000. "Inspiration for optimization from social insect behaviour," Nature, Nature, vol. 406(6791), pages 39-42, July.
    • Handle: RePEc:nat:nature:v:406:y:2000:i:6791:d:10.1038_35017500
    DOI: 10.1038/35017500
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    Cited by:

    1. Chia Hsiang Chen & Vincent Gau & Donna D Zhang & Joseph C Liao & Fei-Yue Wang & Pak Kin Wong, 2010. "Statistical Metamodeling for Revealing Synergistic Antimicrobial Interactions," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-7, November.
    2. Xiaoqing Zhao & Qifa Yue & Jianchao Pei & Junwei Pu & Pei Huang & Qian Wang, 2021. "Ecological Security Pattern Construction in Karst Area Based on Ant Algorithm," IJERPH, MDPI, vol. 18(13), pages 1-21, June.
    3. Luo, Hao & Du, Bing & Huang, George Q. & Chen, Huaping & Li, Xiaolin, 2013. "Hybrid flow shop scheduling considering machine electricity consumption cost," International Journal of Production Economics, Elsevier, vol. 146(2), pages 423-439.
    4. R. Montemanni & L. M. Gambardella & A. E. Rizzoli & A. V. Donati, 2005. "Ant Colony System for a Dynamic Vehicle Routing Problem," Journal of Combinatorial Optimization, Springer, vol. 10(4), pages 327-343, December.
    5. Haitao Xu & Pan Pu & Feng Duan, 2018. "Dynamic Vehicle Routing Problems with Enhanced Ant Colony Optimization," Discrete Dynamics in Nature and Society, Hindawi, vol. 2018, pages 1-13, February.
    6. Liying Xu & Jiadi Zhu & Bing Chen & Zhen Yang & Keqin Liu & Bingjie Dang & Teng Zhang & Yuchao Yang & Ru Huang, 2022. "A distributed nanocluster based multi-agent evolutionary network," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    7. Scianna, Marco, 2024. "The AddACO: A bio-inspired modified version of the ant colony optimization algorithm to solve travel salesman problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 218(C), pages 357-382.
    8. Gao, Shangce & Wang, Yirui & Cheng, Jiujun & Inazumi, Yasuhiro & Tang, Zheng, 2016. "Ant colony optimization with clustering for solving the dynamic location routing problem," Applied Mathematics and Computation, Elsevier, vol. 285(C), pages 149-173.
    9. Carsten Gottschlich & Dominic Schuhmacher, 2014. "The Shortlist Method for Fast Computation of the Earth Mover's Distance and Finding Optimal Solutions to Transportation Problems," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-10, October.

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