A Biologically Inspired Network Design Model

A network design problem is to select a subset of links in a transport network that satisfy passengers or cargo transportation demands while minimizing the overall costs of the transportation. We propose a mathematical model of the foraging behaviour of slime mould P. polycephalum to solve the network design problem and construct optimal transport networks. In our algorithm, a traffic flow between any two cities is estimated using a gravity model. The flow is imitated by the model of the slime mould. The algorithm model converges to a steady state, which represents a solution of the problem. We validate our approach on examples of major transport networks in Mexico and China. By comparing networks developed in our approach with the man-made highways, networks developed by the slime mould, and a cellular automata model inspired by slime mould, we demonstrate the flexibility and efficiency of our approach.

[1]  Michel Minoux,et al.  Exact solution of multicommodity network optimization problems with general step cost functions , 1999, Oper. Res. Lett..

[2]  Feng Xiao,et al.  Managing bottleneck congestion with tradable credits , 2013 .

[3]  Anne Peguin-Feissolle,et al.  The "distance-varying" gravity model in international economics: is the distance an obstacle to trade? , 2009 .

[4]  Yuxin Zhao,et al.  Slime mould imitates transport networks in China , 2013, Int. J. Intell. Comput. Cybern..

[5]  Woo-Sung Jung,et al.  Intercity express bus flow in Korea and its network analysis , 2012 .

[6]  Hai Yang,et al.  Managing congestion and emissions in road networks with tolls and rebates , 2012 .

[7]  Marta C. González,et al.  A universal model for mobility and migration patterns , 2011, Nature.

[8]  Randy B Machemehl,et al.  Optimal Transit Route Network Design Problem with Variable Transit Demand: Genetic Algorithm Approach , 2006 .

[9]  Hai Yang,et al.  Bisection-based trial-and-error implementation of marginal cost pricing and tradable credit scheme , 2012 .

[10]  T. Ueda,et al.  Interaction between cell shape and contraction pattern in the Physarum plasmodium. , 2000, Biophysical chemistry.

[11]  William J. Wolfe,et al.  Three Scheduling Algorithms Applied to the Earth Observing Systems Domain , 2000 .

[12]  Céline Carrère,et al.  Revisiting the effects of regional trade agreements on trade flows with proper specification of the gravity model , 2006 .

[13]  Genaro Juárez Martínez,et al.  Approximating Mexican highways with slime mould , 2010, Natural Computing.

[14]  R J Full,et al.  How animals move: an integrative view. , 2000, Science.

[15]  A. Tero,et al.  Rules for Biologically Inspired Adaptive Network Design , 2010, Science.

[16]  T. Magnanti,et al.  A Dual-Based Algorithm for Multi-Level Network Design , 1994 .

[17]  Guo Shuang Gray Interval Prediction of Air Traffic Flow of Capital Airport , 2007 .

[18]  J. Deneubourg,et al.  The influence of the physical environment on the self-organised foraging patterns of ants , 2001, Naturwissenschaften.

[19]  Haipeng Peng,et al.  Robustness of Interrelated Traffic Networks to Cascading Failures , 2014, Scientific Reports.

[20]  S. P. Evans A relationship between the gravity model for trip distribution and the transportation problem in linear programming , 1973 .

[21]  Natalio Krasnogor,et al.  Nature‐inspired cooperative strategies for optimization , 2009, Int. J. Intell. Syst..

[22]  Adolfas Baublys,et al.  Improvement of external transport cost evaluation in the context of Lithuania’s integration into the European Union , 2005 .

[23]  Boleslaw K. Szymanski,et al.  Threshold-limited spreading in social networks with multiple initiators , 2013, Scientific Reports.

[24]  Chi-Chun Lo,et al.  A multiobjective hybrid genetic algorithm for the capacitated multipoint network design problem , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[25]  Michail-Antisthenis I. Tsompanas,et al.  Evolving Transport Networks With Cellular Automata Models Inspired by Slime Mould , 2015, IEEE Transactions on Cybernetics.

[26]  Vedat Verter,et al.  A Path-Based Approach for Hazmat Transport Network Design , 2008, Manag. Sci..

[27]  L. Perlemuter [From theory to practice]. , 1997, Soins. Psychiatrie.

[28]  Mir Saman Pishvaee,et al.  A memetic algorithm for bi-objective integrated forward/reverse logistics network design , 2010, Comput. Oper. Res..

[29]  S. Stephenson,et al.  Myxomycetes: A Handbook of Slime Molds , 1994 .

[30]  Carlo Mannino,et al.  GUB Covers and Power-Indexed Formulations for Wireless Network Design , 2010, Manag. Sci..

[31]  G. Spindler,et al.  An Integrative View , 1992 .

[32]  Chia-Feng Juang,et al.  Designing Fuzzy-Rule-Based Systems Using Continuous Ant-Colony Optimization , 2010, IEEE Transactions on Fuzzy Systems.

[33]  Michael Florian,et al.  Exact and approximate algorithms for optimal network design , 1979, Networks.

[34]  Germán Terrazas,et al.  Nature Inspired Cooperative Strategies for Optimization, NICSO 2010, May 12-14, 2010, Granada, Spain , 2012, NISCO.

[35]  Andrew Schumann,et al.  PHYSARUM SPATIAL LOGIC , 2011 .

[36]  T. Nakagaki,et al.  Path finding by tube morphogenesis in an amoeboid organism. , 2001, Biophysical chemistry.

[37]  Andrew Adamatzky,et al.  Physarum Machines: Computers from Slime Mould , 2010 .

[38]  Gary A. Davis,et al.  Exact local solution of the continuous network design problem via stochastic user equilibrium assignment , 1994 .

[39]  S. Barbarossa,et al.  Bio-Inspired Sensor Network Design , 2007, IEEE Signal Processing Magazine.

[40]  Kurt Mehlhorn,et al.  Physarum can compute shortest paths , 2011, SODA.

[41]  Zoltán Toroczkai,et al.  Predicting commuter flows in spatial networks using a radiation model based on temporal ranges , 2014, Nature Communications.

[42]  Antonio Mauttone,et al.  A route set construction algorithm for the transit network design problem , 2009, Comput. Oper. Res..

[43]  Tomohiro Shirakawa,et al.  On Simultaneous Construction of Voronoi Diagram and Delaunay Triangulation by Physarum polycephalum , 2009, Int. J. Bifurc. Chaos.

[44]  Jan Karel Lenstra,et al.  The complexity of the network design problem , 1978, Networks.

[45]  Antoine Allard,et al.  Global efficiency of local immunization on complex networks , 2012, Scientific Reports.

[46]  Wei Chen,et al.  Highly Efficient Light-Trapping Structure Design Inspired By Natural Evolution , 2013, Scientific Reports.

[47]  Antonio Bicchi,et al.  On optimal cooperative conflict resolution for air traffic management systems , 2000, IEEE Trans. Intell. Transp. Syst..

[48]  Andrew Adamatzky,et al.  Physarum in silicon: the Greek motorways study , 2014, Natural Computing.

[49]  Andrew Adamatzky,et al.  Physarum Machine: Implementation of a Kolmogorov-Uspensky Machine on a Biological substrate , 2007, Parallel Process. Lett..

[50]  S. Mohan,et al.  TRANSIT ROUTE NETWORK DESIGN USING FREQUENCY CODED GENETIC ALGORITHM , 2003 .

[51]  Giuseppe Nicosia,et al.  Nature Inspired Cooperative Strategies for Optimization (NICSO 2007) (Studies in Computational Intelligence) (Studies in Computational Intelligence) XXXX , 2008 .

[52]  Hossain Poorzahedy,et al.  Hybrid meta-heuristic algorithms for solving network design problem , 2007, Eur. J. Oper. Res..

[53]  A. Tero,et al.  A mathematical model for adaptive transport network in path finding by true slime mold. , 2007, Journal of theoretical biology.

[54]  Christine Solnon,et al.  Ants can solve constraint satisfaction problems , 2002, IEEE Trans. Evol. Comput..

[55]  Chia-Feng Juang,et al.  A hybrid of genetic algorithm and particle swarm optimization for recurrent network design , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[56]  Yu Nie,et al.  Transaction costs and tradable mobility credits , 2012 .

[57]  V Latora,et al.  Efficient behavior of small-world networks. , 2001, Physical review letters.

[58]  Andrew Adamatzky Physarum machine: Implementation of Kolmogorov-Uspensky machine in biological substrat , 2007, ArXiv.

[59]  N. F. Stewart,et al.  The Gravity Model in Transportation Analysis - Theory and Extensions , 1990 .

[60]  Teodor Gabriel Crainic,et al.  Service network design in freight transportation , 2000, Eur. J. Oper. Res..

[61]  Z. Néda,et al.  Human Mobility in a Continuum Approach , 2012, PloS one.

[62]  Özgür B. Akan,et al.  Bio-inspired networking: from theory to practice , 2010, IEEE Communications Magazine.

[63]  F. Chan,et al.  IFSJSP: A novel methodology for the Job-Shop Scheduling Problem based on intuitionistic fuzzy sets , 2013 .

[64]  S. Raghavan,et al.  Long-Distance Access Network Design , 2004, Manag. Sci..

[65]  Hai Yang,et al.  Managing network mobility with tradable credits , 2011 .

[66]  Masashi Aono,et al.  Robust and emergent Physarum logical-computing. , 2004, Bio Systems.

[67]  T. Nakagaki,et al.  Intelligence: Maze-solving by an amoeboid organism , 2000, Nature.

[68]  Xiaoning Zhang,et al.  Improving travel efficiency by parking permits distribution and trading , 2011 .

[69]  Geoffrey D. Gosling,et al.  Air transportation demand forecasts in emerging market economies: a case study of the Kyrgyz Republic in the former Soviet Union , 1998 .