Literature DB >> 24586616

Transportation network with fluctuating input/output designed by the bio-inspired Physarum algorithm.

Shin Watanabe1, Atsuko Takamatsu1.   

Abstract

In this paper, we propose designing transportation network topology and traffic distribution under fluctuating conditions using a bio-inspired algorithm. The algorithm is inspired by the adaptive behavior observed in an amoeba-like organism, plasmodial slime mold, more formally known as plasmodium of Physarum plycephalum. This organism forms a transportation network to distribute its protoplasm, the fluidic contents of its cell, throughout its large cell body. In this process, the diameter of the transportation tubes adapts to the flux of the protoplasm. The Physarum algorithm, which mimics this adaptive behavior, has been widely applied to complex problems, such as maze solving and designing the topology of railroad grids, under static conditions. However, in most situations, environmental conditions fluctuate; for example, in power grids, the consumption of electric power shows daily, weekly, and annual periodicity depending on the lifestyles or the business needs of the individual consumers. This paper studies the design of network topology and traffic distribution with oscillatory input and output traffic flows. The network topology proposed by the Physarum algorithm is controlled by a parameter of the adaptation process of the tubes. We observe various rich topologies such as complete mesh, partial mesh, Y-shaped, and V-shaped networks depending on this adaptation parameter and evaluate them on the basis of three performance functions: loss, cost, and vulnerability. Our results indicate that consideration of the oscillatory conditions and the phase-lags in the multiple outputs of the network is important: The building and/or maintenance cost of the network can be reduced by introducing the oscillating condition, and when the phase-lag among the outputs is large, the transportation loss can also be reduced. We use stability analysis to reveal how the system exhibits various topologies depending on the parameter.

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Year:  2014        PMID: 24586616      PMCID: PMC3935870          DOI: 10.1371/journal.pone.0089231

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  10 in total

1.  Disturbances in a power transmission system

Authors: 
Journal:  Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics       Date:  2000-05

2.  Maze-solving by an amoeboid organism.

Authors:  T Nakagaki; H Yamada; A Tóth
Journal:  Nature       Date:  2000-09-28       Impact factor: 49.962

3.  Path finding by tube morphogenesis in an amoeboid organism.

Authors:  T Nakagaki; H Yamada; A Tóth
Journal:  Biophys Chem       Date:  2001-08-30       Impact factor: 2.352

4.  Obtaining multiple separate food sources: behavioural intelligence in the Physarum plasmodium.

Authors:  Toshiyuki Nakagaki; Ryo Kobayashi; Yasumasa Nishiura; Tetsuo Ueda
Journal:  Proc Biol Sci       Date:  2004-11-07       Impact factor: 5.349

5.  A mathematical model for adaptive transport network in path finding by true slime mold.

Authors:  Atsushi Tero; Ryo Kobayashi; Toshiyuki Nakagaki
Journal:  J Theor Biol       Date:  2006-07-24       Impact factor: 2.691

6.  Structure and formation of ant transportation networks.

Authors:  Tanya Latty; Kai Ramsch; Kentaro Ito; Toshiyuki Nakagaki; David J T Sumpter; Martin Middendorf; Madeleine Beekman
Journal:  J R Soc Interface       Date:  2011-02-02       Impact factor: 4.118

7.  Complex systems analysis of series of blackouts: cascading failure, critical points, and self-organization.

Authors:  Ian Dobson; Benjamin A Carreras; Vickie E Lynch; David E Newman
Journal:  Chaos       Date:  2007-06       Impact factor: 3.642

8.  Environment-dependent morphology in plasmodium of true slime mold Physarum polycephalum and a network growth model.

Authors:  Atsuko Takamatsu; Eri Takaba; Ginjiro Takizawa
Journal:  J Theor Biol       Date:  2008-09-27       Impact factor: 2.691

9.  Rules for biologically inspired adaptive network design.

Authors:  Atsushi Tero; Seiji Takagi; Tetsu Saigusa; Kentaro Ito; Dan P Bebber; Mark D Fricker; Kenji Yumiki; Ryo Kobayashi; Toshiyuki Nakagaki
Journal:  Science       Date:  2010-01-22       Impact factor: 47.728

10.  Traffic optimization in railroad networks using an algorithm mimicking an amoeba-like organism, Physarum plasmodium.

Authors:  Shin Watanabe; Atsushi Tero; Atsuko Takamatsu; Toshiyuki Nakagaki
Journal:  Biosystems       Date:  2011-05-19       Impact factor: 1.973

  10 in total
  2 in total

1.  Cell fusion through slime mould network dynamics.

Authors:  Sheryl Hsu; Laura P Schaposnik
Journal:  J R Soc Interface       Date:  2022-04-27       Impact factor: 4.293

Review 2.  Sender-receiver systems and applying information theory for quantitative synthetic biology.

Authors:  Diego Barcena Menendez; Vivek Raj Senthivel; Mark Isalan
Journal:  Curr Opin Biotechnol       Date:  2014-10-01       Impact factor: 9.740

  2 in total

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