Skip to main content
SpringerLink
Log in

Swap-Based Discrete Firefly Algorithm for Traveling Salesman Problem

  • Conference paper
  • First Online:
Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10607))

Abstract

Firefly algorithm (FA) is an emerging nature-inspired algorithm which has been used to solve discrete optimization problems such as traveling salesman problem (TSP). However, during the discretization of firefly algorithm, one of the FA’s characteristics, i.e. the movement of a dimmer firefly towards a brighter firefly is unapparent as the movement are random. Thus, in this paper, the usage of swap operation as the movement strategy is proposed. The proposed algorithm, Swap-based Discrete Firefly Algorithm (SDFA), is then integrated with Nearest-Neighborhood initialization, reset strategy and Fixed Radius Near Neighbor 2-opt operator (FRNN 2-opt). The proposed algorithm is tested on 45 TSP instances and is compared with several states-of-the-art algorithm. The findings of this research show that the proposed algorithm performs competitively compared to the Discrete Firefly Algorithm, the Discrete Cuckoo Search, the Discrete Bat Algorithm, the Hybrid Genetic Algorithm and the Discrete Bacterial Memetic Evolutionary Algorithm. On average, SDFA reports a percentage deviation of 0.02% from known optimum for TSP instances with dimension range from 14 to 318 cities.

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

Notes

  1. 1.

    http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/.

References

  1. Wong, L.P., Low, M.Y.H., Chong, C.S.: Bee colony optimization with local search for traveling salesman problem. In: Proceedings of 6th IEEE International Conference on Industrial Informatics, INDIN 2008, pp. 1019–1025 (2008)

    Google Scholar 

  2. Applegate, D.L., Bixby, R.E., Chvátal, V., Cook, W.J.: The Traveling Salesman Problem. Princeton University Press, Princeton (2007)

    MATH  Google Scholar 

  3. Lin, S., Kernighan, B.W.: An effective heuristic algorithm for the traveling-salesman problem. Oper. Res. 21(2), 498–516 (1973)

    Article  MATH  MathSciNet  Google Scholar 

  4. Helsgaun, K.: An effective implementation of the lin-kernighan traveling salesman heuristic. Eur. J. Oper. Res. 126(1), 106–130 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  5. Yang, X.S.: Firefly algorithms for multimodal optimization. In: Proceedings of 5th International Symposium on Stochastic Algorithms: Foundations and Applications, SAGA 2009, pp. 169–178 (2009)

    Google Scholar 

  6. Yang, X.S.: Nature-Inspired Optimization Algorithms. Luniver Press, Bristol (2010)

    MATH  Google Scholar 

  7. Yang, X.S.: Metaheuristic optimization: algorithm analysis and open problems. In: Proceedings of 10th International Symposium on Experimental Algorithms, SEA 2011, pp. 21–32 (2011)

    Google Scholar 

  8. Jati, G.K., Suyanto: Evolutionary discrete firefly algorithm for travelling salesman problem. In: Bouchachia, A. (ed.) ICAIS 2011. LNCS, vol. 6943, pp. 393–403. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23857-4_38

  9. Kumbharana, S.N., Pandey, G.M.: Solving travelling salesman problem using firefly algorithm. Int. J. Res. Sci. Adv. Technol. 2(2), 53–57 (2013)

    Google Scholar 

  10. Jati, G.K., Manurung, R., Suyanto: Discrete firefly algorithm for traveling salesman problem: a new movement scheme. In: Yang, X.S., Cui, Z., Xiao, R., Gandomi, A.H., Karamanoglu, M. (eds.) Swarm Intelligence and Bio-Inspired Computation, pp. 295–312. Elsevier, Amsterdam (2013)

    Google Scholar 

  11. Osaba, E., Carballedo, R., Diaz, F., Onieva, E., Lopez, P., Perallos, A.: On the influence of using initialization functions on genetic algorithms solving combinatorial optimization problems: a first study on the TSP. In: Proceedings of IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2014, pp. 1–6 (2014)

    Google Scholar 

  12. Mahi, M., Baykan, Ö.K., Kodaz, H.: A new hybrid method based on particle swarm optimization, ant colony optimization and 3-opt algorithms for traveling salesman problem. Appl. Soft Comput. 30, 484–490 (2015)

    Article  Google Scholar 

  13. Alijla, B.O., Wong, L.P., Lim, C.P., Khader, A.T., Al-Betar, M.A.: An ensemble of intelligent water drop algorithms and its application to optimization problems. Inf. Sci. 325, 175–189 (2015)

    Article  Google Scholar 

  14. Wong, L.P., Low, M.Y.H., Chong, C.S.: Bee colony optimization with local search for traveling salesman problem. Int. J. Artif. Intell. Tools 19(03), 305–334 (2010)

    Article  Google Scholar 

  15. Tüű-Szabó, B., Földesi, P., Kóczy, L.T.: Improved discrete bacterial memetic evolutionary algorithm for the traveling salesman problem. In: Proceedings of Computational Intelligence in Information Systems Conference, CIIS 2016, pp. 27–38 (2017)

    Google Scholar 

  16. Kóczy, L.T., Földesi, P., Tüű-Szabó, B.: An effective discrete bacterial memetic evolutionary algorithm for the traveling salesman problem. Int. J. Intell. Syst. 32(8), 862–876 (2017)

    Article  Google Scholar 

  17. Fister, I., Fister, I., Yang, X.S., Brest, J.: A comprehensive review of firefly algorithms. Swarm Evol. Comput. 13, 34–46 (2013)

    Article  Google Scholar 

  18. Wong, L.P., Low, M.Y.H., Chong, C.S.: An efficient bee colony optimization algorithm for traveling salesman problem using frequency-based pruning. In: Proceedings of 7th IEEE International Conference on Industrial Informatics, INDIN 2009, pp. 775–782 (2009)

    Google Scholar 

  19. Eiben, A.E., Smit, S.K.: Evolutionary algorithm parameters and methods to tune them. In: Hamadi, Y., Monfroy, E., Saubion, F. (eds.) Autonomous Search, pp. 15–36. Springer, Berlin Heidelberg (2012). doi:10.1007/978-3-642-21434-9_2

    Google Scholar 

  20. Ouaarab, A., Ahiod, B., Yang, X.S.: Discrete cuckoo search algorithm for the travelling salesman problem. Neural Comput. Appl. 24(7), 1659–1669 (2014)

    Article  Google Scholar 

  21. Saji, Y., Riffi, M.E.: A novel discrete bat algorithm for solving the travelling salesman problem. Neural Comput. Appl. 27(7), 1853–1866 (2016)

    Article  Google Scholar 

  22. Wang, Y.: The hybrid genetic algorithm with two local optimization strategies for traveling salesman problem. Comput. Ind. Eng. 70, 124–133 (2014)

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported by the Research University Grant (Grant No: 1001/PKOMP/814274) at Universiti Sains Malaysia (USM). Also, the first author acknowledges USM for the fellowship scheme to study Ph.D. degree at USM.

Author information

Authors and Affiliations

  1. School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia

    How Siang Chuah, Li-Pei Wong & Fadratul Hafinaz Hassan

Authors
  1. How Siang Chuah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li-Pei Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fadratul Hafinaz Hassan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to How Siang Chuah .

Editor information

Editors and Affiliations

  1. Universiti Teknologi Brunei, Gadong, Brunei Darussalam

    Somnuk Phon-Amnuaisuk

  2. Universiti Teknologi Brunei, Gadong, Brunei Darussalam

    Swee-Peng Ang

  3. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Soo-Young Lee

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Chuah, H.S., Wong, LP., Hassan, F.H. (2017). Swap-Based Discrete Firefly Algorithm for Traveling Salesman Problem. In: Phon-Amnuaisuk, S., Ang, SP., Lee, SY. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2017. Lecture Notes in Computer Science(), vol 10607. Springer, Cham. https://doi.org/10.1007/978-3-319-69456-6_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-69456-6_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69455-9

  • Online ISBN: 978-3-319-69456-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation