Skip to main content
SpringerLink
Log in

Research on the Shortest Path Problem Based on Improved Genetic Algorithm

  • Conference paper
  • First Online:
Human Centered Computing (HCC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10745))

Included in the following conference series:

  • 1729 Accesses

Abstract

Aiming at the complex large-scale shortest path problem, a modified genetic learning algorithm has been offered. Firstly, in order to prevent premature convergence of evolution population, a new fitness function for the shortest path is proposed. At the same time, to ensure the global search ability of the algorithm a selection method of crossover probability is proposed. And to ensure the local search ability of genetic algorithm a selection method of mutation probability is proposed. The experimental results show that the modified genetic algorithm has a better success rate compared with the traditional 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. Ko, Y.D., Jang, Y.J., Min, S.L.: The optimal economic design of the wireless powered intelligent transportation system using genetic algorithm considering nonlinear cost function. Comput. Ind. Eng. 89(C), 67–79 (2015)

    Article  Google Scholar 

  2. Chitra, C., Subbaraj, P.: A nondominated sorting genetic algorithm solution for shortest path routing problem in computer networks. Expert Syst. Appl. 39(1), 1518–1525 (2012)

    Article  Google Scholar 

  3. Cheng, H.: Obstacle avoidance shortest path algorithm and its application. Electron. Des. Eng. 16, 56–60 (2013). (in Chinese)

    Google Scholar 

  4. Qi, X., Liu, S.: Selection algorithm for QoS routing based on k-shortest paths. J. Jilin Univ. (Eng. Technol. Ed.) 05, 526–530 (2005). (in Chinese)

    Google Scholar 

  5. Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1(1), 269–271 (1959)

    Article  MathSciNet  MATH  Google Scholar 

  6. Bellman, R.: On a routing problem. Q. Appl. Math. 16, 87–90 (1958)

    Article  MathSciNet  MATH  Google Scholar 

  7. Lin, L., Gen, M.: Priority-based genetic algorithm for shortest path routing problem in OSPF. Stud. Comput. Intell. 187, 91–103 (2009)

    MATH  Google Scholar 

  8. Holland, J.H.: Adaptation in Natural Artificial Systems, pp. 1–17. MIT Press, Cambridge (1975)

    Google Scholar 

  9. Cao, Z.: The study on exhaust algorithm, search algorithm, dynamic design for 0–1 Knapsack problem. Comput. Knowl. Technol. 5(12), 3193–3198 (2009). (in Chinese)

    Google Scholar 

  10. Srinivas, M., Patnaik, L.: Adaptive probabilities of crossover and mutation in genetic algorithm. IEEE Trans. Syst. Man Cybern. 24(4), 656–666 (1994)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Information and Network Center, Tianjin University, Tianjin, 300072, China

    Baoliang Wang, Kaining Lu & Huizhen Zhao

  2. School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China

    Susu Yao

Authors
  1. Baoliang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Susu Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kaining Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huizhen Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Baoliang Wang .

Editor information

Editors and Affiliations

  1. Wuhan University of Technology, Wuhan, China

    Qiaohong Zu

  2. Fujitsu Laboratories of Europe Ltd., Hayes, United Kingdom

    Bo Hu

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, B., Yao, S., Lu, K., Zhao, H. (2018). Research on the Shortest Path Problem Based on Improved Genetic Algorithm. In: Zu, Q., Hu, B. (eds) Human Centered Computing. HCC 2017. Lecture Notes in Computer Science(), vol 10745. Springer, Cham. https://doi.org/10.1007/978-3-319-74521-3_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-74521-3_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74520-6

  • Online ISBN: 978-3-319-74521-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation