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The Role of Largest Connected Components in Collective Motion

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Book cover Swarm Intelligence (ANTS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11172))

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Abstract

Systems showing collective motion are partially described by a distribution of positions and a distribution of velocities. While models of collective motion often focus on system features governed mostly by velocity distributions, the model presented in this paper also incorporates features influenced by positional distributions. A significant feature, the size of the largest connected component of the graph induced by the particle positions and their perception range, is identified using a 1-d self-propelled particle model (SPP). Based on largest connected components, properties of the system dynamics are found that are time-invariant. A simplified macroscopic model can be defined based on this time-invariance, which may allow for simple, concise, and precise predictions of systems showing collective motion.

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Notes

  1. 1.

    https://doi.org/10.5281/zenodo.1293372.

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Authors and Affiliations

  1. Institute of Computer Engineering, University of Lübeck, Lübeck, Germany

    Heiko Hamann

Authors
  1. Heiko Hamann
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Correspondence to Heiko Hamann .

Editor information

Editors and Affiliations

  1. Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. Université Libre de Bruxelles, Brussels, Belgium

    Mauro Birattari

  3. Artificial Intelligence Research Institute, Bellaterra, Spain

    Christian Blum

  4. University of Southern Denmark, Odense, Denmark

    Anders L. Christensen

  5. University of Sheffield, Sheffield, UK

    Andreagiovanni Reina

  6. National Research Council, Rome, Italy

    Vito Trianni

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Hamann, H. (2018). The Role of Largest Connected Components in Collective Motion. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A., Reina, A., Trianni, V. (eds) Swarm Intelligence. ANTS 2018. Lecture Notes in Computer Science(), vol 11172. Springer, Cham. https://doi.org/10.1007/978-3-030-00533-7_23

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  • DOI: https://doi.org/10.1007/978-3-030-00533-7_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00532-0

  • Online ISBN: 978-3-030-00533-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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