Skip to main content
SpringerLink
Log in

A Novel Physarum-Based Optimization Algorithm for Shortest Path

  • Conference paper
  • First Online:
Advances in Swarm Intelligence (ICSI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12689))

Included in the following conference series:

  • 1133 Accesses

Abstract

As a new bio-inspired algorithm, the Physarum-based algorithm has shown great performance for solving complex computational problems. More and more researchers try to use the algorithm to solve some network optimization problems. Although the Physarum-based algorithm can figure out these problems correctly and accurately, the convergence speed of Physarum-based algorithm is relatively slow. This is mainly because many linear equations have to be solved when applying Physarum-based algorithm. Furthermore, many iterations are required using Physarum-based algorithm for network optimization problems with large number of nodes. With those observations in mind, two new methods are proposed to deal with these problems. By observing the traffic network data, there are many redundant nodes, which don’t need to be computed in practical applications. The calculation time of the algorithm is reduced by avoiding these special nodes. The convergence speed of Physarum-based algorithm can then be accelerated. Two real traffic networks and eighteen random sparse connected graphs are used to verify the performance of the proposed algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Transportation networks. https://github.com/bstabler/TransportationNetworks

  2. Adamatzky, A.: If bz medium did spanning trees these would be the same trees as physarum built. Phys. Lett. A 373(10), 952–956 (2009)

    Article  Google Scholar 

  3. Adamatzky, A., Jones, J.: Towards physarum robots: computing and manipulating on water surface. J. Bionic Eng. 5(4), 348–357 (2008)

    Article  Google Scholar 

  4. Gunji, Y.P., Shirakawa, T., Niizato, T., Haruna, T.: Minimal model of a cell connecting amoebic motion and adaptive transport networks. J. Theor. Biol. 253(4), 659–667 (2008)

    Article  Google Scholar 

  5. Gunji, Y.P., Shirakawa, T., Niizato, T., Yamachiyo, M., Tani, I.: An adaptive and robust biological network based on the vacant-particle transportation model. J. Theor. Biol. 272(1), 187–200 (2011)

    Article  Google Scholar 

  6. Jones, J.: Characteristics of pattern formation and evolution in approximations of physarum transport networks. Artif. Life 16(2), 127–153 (2010)

    Article  Google Scholar 

  7. Jones, J.: A morphological adaptation approach to path planning inspired by slime mould. Int. J. Gen Syst 44(3), 279–291 (2015)

    Article  MathSciNet  Google Scholar 

  8. Jones, J.: Applications of multi-agent slime mould computing. Int. J. Parallel Emergent Distrib. Syst. 31(5), 420–449 (2016)

    Article  Google Scholar 

  9. Jones, J., Adamatzky, A.: Computation of the travelling salesman problem by a shrinking blob. Nat. Comput. 13(1), 1–16 (2013). https://doi.org/10.1007/s11047-013-9401-x

    Article  MathSciNet  Google Scholar 

  10. Kershaw, D.S.: The incomplete cholesky–conjugate gradient method for the iterative solution of systems of linear equations. J. Comput. Phys. 26(1), 43–65 (1978)

    Article  MathSciNet  Google Scholar 

  11. Liang, M., Gao, C., Liu, Y., Tao, L., Zhang, Z.: A new physarum network based genetic algorithm for bandwidth-delay constrained least-cost multicast routing. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds.) ICSI 2015. LNCS, vol. 9141, pp. 273–280. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20472-7_29

    Chapter  Google Scholar 

  12. Liu, Y., et al.: Solving np-hard problems with physarum-based ant colony system. IEEE/ACM Trans. Comput. Biol. Bioinf. 14(1), 108–120 (2015)

    Article  Google Scholar 

  13. Ma, Q., Johansson, A., Tero, A., Nakagaki, T., Sumpter, D.J.: Current-reinforced random walks for constructing transport networks. J. R. Soc. Interface 10(80), 20120864 (2013)

    Article  Google Scholar 

  14. Masi, L., Vasile, M.: A multi-directional modified physarum algorithm for optimal multi-objective discrete decision making. In: Schuetze O. et al. (eds) EVOLVE-A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation III, pp. 195–212. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-319-01460-9_9

  15. Pershin, Y.V., Di Ventra, M.: Solving mazes with memristors: a massively parallel approach. Phys. Rev. E 84(4), 046703 (2011)

    Google Scholar 

  16. Qian, T., Zhang, Z., Gao, C., Wu, Y., Liu, Y.: An ant colony system based on the physarum network. In: Tan, Y., Shi, Y., Mo, H. (eds.) ICSI 2013. LNCS, vol. 7928, pp. 297–305. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38703-6_35

    Chapter  Google Scholar 

  17. Tero, A., Kobayashi, R., Nakagaki, T.: Physarum solver: a biologically inspired method of road-network navigation. Phys. A 363(1), 115–119 (2006)

    Article  Google Scholar 

  18. Tero, A., et al.: Rules for biologically inspired adaptive network design. Science 327(5964), 439–442 (2010)

    Google Scholar 

  19. Tero, A., Yumiki, K., Kobayashi, R., Saigusa, T., Nakagaki, T.: Flow-network adaptation in physarum amoebae. Theory Biosci. 127(2), 89–94 (2008)

    Article  Google Scholar 

  20. Toth, A., Nakagaki, T.: Intelligence: maze-solving by an amoeboid organism. Nature 407(28), 470 (2000)

    Google Scholar 

  21. Tsompanas, M.A.I., Sirakoulis, G.C.: Modeling and hardware implementation of an amoeba-like cellular automaton. Bioinspiration Biomim. 7(3), 036013 (2012)

    Google Scholar 

  22. Tsuda, S., Aono, M., Gunji, Y.P.: Robust and emergent physarum logical-computing. Biosystems 73(1), 45–55 (2004)

    Article  Google Scholar 

  23. Wang, H., Lu, X., Zhang, X., Wang, Q., Deng, Y.: A bio-inspired method for the constrained shortest path problem. Sci. World J. 2014, 1–12 (2014)

    Google Scholar 

  24. Zhang, X., Huang, S., Hu, Y., Zhang, Y., Mahadevan, S., Deng, Y.: Solving 0–1 knapsack problems based on amoeboid organism algorithm. Appl. Math. Comput. 219(19), 9959–9970 (2013)

    MathSciNet  MATH  Google Scholar 

  25. Zhang, X., Wang, Q., Adamatzky, A., Chan, F.T., Mahadevan, S., Deng, Y.: A biologically inspired optimization algorithm for solving fuzzy shortest path problems with mixed fuzzy arc lengths. J. Optim. Theory Appl. 163(3), 1049–1056 (2014)

    Article  MathSciNet  Google Scholar 

  26. Zhang, X., Wang, Q., Chan, F.T., Mahadevan, S., Deng, Y.: A physarum polycephalum optimization algorithm for the bi-objective shortest path problem. Int. J. Unconv. Comput. 10, 143–162 (2014)

    Google Scholar 

  27. Zhang, X., Zhang, Y., Hu, Y., Deng, Y., Mahadevan, S.: An adaptive amoeba algorithm for constrained shortest paths. Expert Syst. Appl. 40(18), 7607–7616 (2013)

    Article  Google Scholar 

  28. Zhang, Y., Zhang, Z., Wei, D., Deng, Y.: Centrality measure in weighted networks based on an amoeboid algorithm. J. Inf. Comput. Sci. 9(2), 369–376 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Dan Wang & Zili Zhang

Authors
  1. Dan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zili Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zili Zhang .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, D., Zhang, Z. (2021). A Novel Physarum-Based Optimization Algorithm for Shortest Path. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12689. Springer, Cham. https://doi.org/10.1007/978-3-030-78743-1_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-78743-1_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78742-4

  • Online ISBN: 978-3-030-78743-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation