Skip to main content
SpringerLink
Log in

High Performance Ant Colony Optimizer (HPACO) for Travelling Salesman Problem (TSP)

  • Conference paper
Book cover Advances in Swarm Intelligence (ICSI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8794))

Included in the following conference series:

Abstract

Travelling Salesman Problem (TSP) is a classical combinatorial optimization problem. This problem is NP-hard in nature and is well suited for evaluation of unconventional algorithmic approaches based on natural computation. Ant Colony Optimization (ACO) technique is one of the popular unconventional optimization technique to solve this problem. In this paper, we propose High Performance Ant Colony Optimizer (HPACO) which modifies conventional ACO. The result of implementation shows that our proposed technique has a better performance than the conventional ACO.

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 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. Zhang, J.: Natural Computation for the Traveling Salesman Problem. In: Proceedings of the Second International Conference on Intelligent Computation Technology and Automation (ICICTA), vol. 1, pp. 366–369 (2009)

    Google Scholar 

  2. Wang, L., Wang, D., Ding, N.: Research on BP Neural Network Optimal Method Based on Improved Ant Colony Algorithm. In: Second International Conference on Computer Engineering and Applications (ICCEA), vol. 1, pp. 117–121 (2010)

    Google Scholar 

  3. Dorigo, M., Socha, K.: An Introduction to Ant Colony Optimization. Technical Report IRIDIA 2006-010 (2006)

    Google Scholar 

  4. Dorigo, M., Gambardella, L.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)

    Article  Google Scholar 

  5. Stützle, T., Hoos, H.H.: Max–min ant system. Future Generation Computer Systems 16(8), 889–914 (2000)

    Article  Google Scholar 

  6. Dorigo, M., Maniezzo, V., Colorni, A.: Positive feedback as a search strategy. Technical Report 91 (1991)

    Google Scholar 

  7. Bullnheimer, B., Hartl, R.F., Strauss, C.: A new rank-based version of the ant system: A computational study. Central European Journal for Operations Research and Economics 7(1), 25–38–016 (1999), Dipartimento di Elettronica, Politecnico di Milano, Italy, (1999)

    Google Scholar 

  8. Dorigo, M., Gambardella, L.M.: A study of some properties of Ant-Q. In: Fourth International Conference on Parallel Problem Solving from Nature, Berlin, pp. 656–665 (1996)

    Google Scholar 

  9. Fogel, D.: Applying evolutionary programming to selected traveling salesman problems. Cybernetics and Systems: An International Journal 24, 27–36 (1993)

    Article  MathSciNet  Google Scholar 

  10. Bersini, H., Oury, C., Dorigo, M.: Hybridization of Genetic Algorithms.,Tech. Rep. No. IRIDIA 95-22, IRIDIA, Université Libre de Bruxelles, Belgium (1995)

    Google Scholar 

  11. Whitley, D., Starkweather, T., Fuquay, D.: Scheduling problems and travelling salesman: The geneticedge recombination operator. In: Schaffer, J.D. (ed.) Proceedings of the Third International Conference on Genetic Algorithms, pp. 133–140 (1989)

    Google Scholar 

  12. Lin, F.–T., Kao, C.–Y., Hsu, C.–C.: Applying the genetic approach to simulated annealing in solving some NP-hard problems. IEEE Transactions on Systems, Man, and Cybernetics 23, 1752–1767 (1993)

    Article  Google Scholar 

  13. Oliver, I., Smith, D., Holland, J.R.: A study of permutation crossover operators on the travelling salesman problem. In: Grefenstette, J.J. (ed.) Proceedings of the Second International Conference on Genetic Algorithms, pp. 224–230 (1987)

    Google Scholar 

  14. Eilon, S., Watson-Gandy, C.D.T., Christofides, N.: Distribution management: mathematical modeling and practical analysis. Operational Research Quarterly 20, 37–53 (1969)

    Article  Google Scholar 

  15. Jayalakshmi, G.A., Sathiamoorthy, S.: A hybrid genetic algorithm – A new approach tosolve traveling salesman problem. International Journal of Computational Engineering Science 2, 339–355 (2001)

    Article  Google Scholar 

  16. Sahana, S.K., Jain, A.: An Improved Modular Hybrid Ant Colony Approach for Solving Traveling Salesman Problem. International Journal on Computing (JoC) 1(2), 123–127 (2011), doi: 10.5176-2010-2283_1.249, ISSN: 2010-2283

    Google Scholar 

  17. Sahana, S.K., Jain, A., Mustafi, A.: A Comparative Study on Multicast Routing using Dijkstra’s, Prims and Ant Colony Systems. International Journal of Computer Engineering & Technology (IJCET) 2(2), 301–310 (2010), ISSN : 0976-6367

    Google Scholar 

  18. Johnson, D.S., McGeoch, L.A., Rothberg, E.E.: Asymptotic Experimental Analysis for the Held-Karp Traveling Salesman Bound. In: Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 341–350 (1996)

    Google Scholar 

  19. Karp, R.M.: Dynamic programming meets the principle of inclusion and exclusion. Oper. Res. Lett. 1(2), 49–51 (1982), doi:10.1016/0167-6377(82)90044-X

    Article  MATH  MathSciNet  Google Scholar 

  20. Wu, Z.L., Zhao, N., Ren, G.H., Quan, T.F.: Population declining ACO algorithm and its applications. Expert Systems with Applications 36(3), 6276–6281 (2009)

    Article  Google Scholar 

  21. Udomsakdigool, A., Kachitvichyanukul, V.: Multiple colony ant algorithm for job-shop sc-heduling problem. International Journal of Production Research 46(15), 4155–4175 (2008)

    Article  MATH  Google Scholar 

  22. Sahana, S.K., Jain, A.: Modified Ant Colony Optimizer (MACO) for the Travelling Salesman Problem. In: Computational Intelligence and Information Technology, CIIT 2012, Chennai, December 3-4. ACEEE Conference Proceedings Series 3 by Elsevier, pp. 267–276 (2012), ISBN:978-93-5107-194-5

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept of CSE, Birla Institute of Technology, Ranchi, Jharkhand, India

    Sudip Kumar Sahana & Aruna Jain

Authors
  1. Sudip Kumar Sahana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aruna Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), Peking University, 100871, Beijing, China

    Ying Tan

  2. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Computer Science Department, CINVESTAV-IPN, Mexico

    Carlos A. Coello Coello

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Sahana, S.K., Jain, A. (2014). High Performance Ant Colony Optimizer (HPACO) for Travelling Salesman Problem (TSP). In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8794. Springer, Cham. https://doi.org/10.1007/978-3-319-11857-4_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11857-4_19

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11856-7

  • Online ISBN: 978-3-319-11857-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation