Skip to main content

An Improved Bean Optimization Algorithm for Solving TSP

  • Conference paper
Advances in Swarm Intelligence (ICSI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7331))

Included in the following conference series:

Abstract

Inspired by the transmission of beans in nature, a novel swarm intelligence algorithm-Bean Optimization Algorithm (BOA) is proposed. In the area of continuous optimization problems solving, BOA has shown a good performance. In this paper, an improved BOA is presented for solving TSP, a typical discrete optimization problem. Two novel evolution mechanisms named population migration and priori information cross-sharing are proposed to improve the performance of BOA. The improved BOA algorithm maintains the basic idea of BOA and overcomes the shortcoming that BOA with continuous distribution function can not be applied to solve the discrete optimization problems. The experimental results of TSP show that the improved BOA algorithm is suit for solving discrete optimization problems with high efficiency.

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

Access this chapter

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Dorigo, M., Birattari, M., Stützle, T.: Ant Colony Optimization– Artificial Ants as a Computational Intelligence Technique. IEEE Computational Intelligence Magazine 11(4), 28–39 (2006)

    Google Scholar 

  2. Kennedy, J.: The Particle Swarm: Social Adaptation of Knowledge. In: 1997 IEEE International Conference on Evolutionary Computation, pp. 303–308. IEEE Press, New York (1997)

    Google Scholar 

  3. Zhang, X., Wang, R., Song, L.: A Novel Evolutionary Algorithm—Seed Optimization Algorithm. Pattern Recognition and Artificial Intelligence 21(5), 677–681 (2008)

    MathSciNet  Google Scholar 

  4. Wang, P., Cheng, Y.: Relief Supplies Scheduling Based on Bean Optimization Algorithm. Economic Research Guide (8), 252–253 (2010)

    Google Scholar 

  5. Zhang, X., Sun, B., Mei, T., Wang, R.: Post-disaster Restoration Based on Fuzzy Preference Relation and Bean Optimization Algorithm. In: 2010 IEEE Youth Conference on Information, Computing and Telecommunications, pp. 253–256. IEEE Press, New York (2010)

    Google Scholar 

  6. Li, Y.: Solving TSP by an ACO-and-BOA-based Hybrid Algorithm. In: 2010 International Conference on Computer Application and System Modeling, pp. 189–192. IEEE Press, New York (2010)

    Google Scholar 

  7. Mona Lisa TSP Challenge, http://www.tsp.gatech.edu/data/ml/mona-lisa100K.tsp

  8. Stützle, T., Hoos, H.: MAX-MIN Ant System. Future Generation Computer Systems 16(8), 889–891 (2000)

    Article  Google Scholar 

  9. Optimization Algorithm Toolkit, http://optalgtoolkit.sourceforge.net

  10. Su, J., Wang, J.: Improved Particle Swarm Optimization for Traveling Salesman Problem. Computer Engineering and Applications 46(4), 52–75 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhang, X., Jiang, K., Wang, H., Li, W., Sun, B. (2012). An Improved Bean Optimization Algorithm for Solving TSP. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30976-2_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-30976-2_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30975-5

  • Online ISBN: 978-3-642-30976-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics