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Physarum-Inspired Solutions to Network Optimization Problems

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Shortest Path Solvers. From Software to Wetware

Part of the book series: Emergence, Complexity and Computation ((ECC,volume 32))

Abstract

In this chapter, we introduce a mathematical model inspired by slime mould Physarum polycephalum, an amoeboid organism that exhibits phenomenal path-finding behavior. By comparing it to one of the classic shortest path algorithms—Dijkstra algorithm, we highlight and summarize the key characteristics that are unique in Physarum algorithm, namely flow continuity and adaptivity. Due to these features, the Physarum model responses autonomously to the changes of external environment, thereby converging to optimal solutions adaptively. Herein, we take advantage of its superior properties and develop various models to address several significant network optimization problems, including traffic flow assignment and supply chain network design. By comparing its performance with the state-of-the-art methods in terms of solution quality and running time, we demonstrate the efficiency of the proposed algorithms.

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Notes

  1. 1.

    This subsection is taken from the paper published in IEEE Transactions on Cybernetics. For more details, please see [47].

  2. 2.

    This subsection is taken from the paper published in Annals of Operations Research. For more details, please see Ref. [58].

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Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, School of Engineering, Vanderbilt University, Nashville, TN, 37235, USA

    Xiaoge Zhang

  2. Department of Electrical Engineering and Computer Science, School of Engineering, Vanderbilt University, Nashville, TN, 37235, USA

    Chao Yan

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  1. Xiaoge Zhang
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Correspondence to Xiaoge Zhang .

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Editors and Affiliations

  1. Unconventional Computing Centre, University of the West of England, Bristol, United Kingdom

    Andrew Adamatzky

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Zhang, X., Yan, C. (2018). Physarum-Inspired Solutions to Network Optimization Problems. In: Adamatzky, A. (eds) Shortest Path Solvers. From Software to Wetware. Emergence, Complexity and Computation, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-319-77510-4_12

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  • DOI: https://doi.org/10.1007/978-3-319-77510-4_12

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