Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Bare Bones Fireworks Algorithm for Capacitated p-Median Problem

  • Conference paper
  • First Online:
Advances in Swarm Intelligence (ICSI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10941))

Included in the following conference series:

Abstract

The p-median problem represents a widely applicable problem in different fields such as operational research and supply chain management. Numerous versions of the p-median problem are defined in literature and it has been shown that it belongs to the class of NP-hard problems. In this paper a recent swarm intelligence algorithm, the bare bones fireworks algorithm, which is the latest version of the fireworks algorithm is proposed for solving capacitated p-median problem. The proposed method is tested on benchmark datasets with different values for p. Performance of the proposed method was compared to other methods from literature and it exhibited competitive results with possibility for further improvements.

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. Martí, R., Corberán, A., Peiró, J.: Scatter search for an uncapacitated p-hub median problem. Comput. Oper. Res. 58, 53–66 (2015)

    Article  MathSciNet  Google Scholar 

  2. Herda, M., Haviar, M., et al.: Hybrid genetic algorithms with selective crossover for the capacitated p-median problem. Cent. Eur. J. Oper. Res. 25, 651–664 (2017)

    Article  MathSciNet  Google Scholar 

  3. Borah, S., Dewan, H.: A hybrid PSO model for solving continuous p-median problem. In: Prasath, R., O’Reilly, P., Kathirvalavakumar, T. (eds.) MIKE 2014. LNCS (LNAI), vol. 8891, pp. 178–188. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13817-6_19

    Chapter  Google Scholar 

  4. Kariv, O., Hakimi, S.L.: An algorithmic approach to network location problems. II: the p-medians. SIAM J. Appl. Math. 37, 539–560 (1979)

    Article  MathSciNet  Google Scholar 

  5. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43. IEEE (1995)

    Google Scholar 

  6. Dorigo, M., Gambardella, L.M.: Ant colonies for the travelling salesman problem. Biosystems 43, 73–81 (1997)

    Article  Google Scholar 

  7. Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04944-6_14

    Chapter  Google Scholar 

  8. Tuba, E., Mrkela, L., Tuba, M.: Support vector machine parameter tuning using firefly algorithm. In: 26th International Conference Radioelektronika, pp. 413–418. IEEE (2016)

    Google Scholar 

  9. Bacanin, N., Tuba, M.: Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint. Sci. World J. 1–16 (2014). Aritcle ID: 721521

    Article  Google Scholar 

  10. Tuba, E., Tuba, M., Beko, M.: Two stage wireless sensor node localization using firefly algorithm. In: Yang, X.-S., Nagar, A.K., Joshi, A. (eds.) Smart Trends in Systems, Security and Sustainability. LNNS, vol. 18, pp. 113–120. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-6916-1_10

    Chapter  Google Scholar 

  11. Wang, G.G., Deb, S., Gao, X.Z., Coelho, L.D.S.: A new metaheuristic optimisation algorithm motivated by elephant herding behaviour. Int. J. Bio-Inspir. Comput. 8, 394–409 (2016)

    Article  Google Scholar 

  12. Tuba, E., Capor-Hrosik, R., Alihodzic, A., Jovanovic, R., Tuba, M.: Chaotic elephant herding optimization algorithm. In: IEEE 16th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 213–216. IEEE (2018)

    Google Scholar 

  13. Tuba, E., Alihodzic, A., Tuba, M.: Multilevel image thresholding using elephant herding optimization algorithm. In: Proceedings of 14th International Conference on the Engineering of Modern Electric Systems (EMES), pp. 240–243 (2017)

    Google Scholar 

  14. Alihodzic, A., Tuba, E., Capor-Hrosik, R., Dolicanin, E., Tuba, M.: Unmanned aerial vehicle path planning problem by adjusted elephant herding optimization. In: 25th Telecommunications Forum (TELFOR), pp. 804–807 (2017)

    Google Scholar 

  15. Shi, Y.: Brain storm optimization algorithm. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011. LNCS, vol. 6728, pp. 303–309. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21515-5_36

    Chapter  Google Scholar 

  16. Tuba, E., Dolicanin, E., Tuba, M.: Chaotic brain storm optimization algorithm. In: Yin, H., Gao, Y., Chen, S., Wen, Y., Cai, G., Gu, T., Du, J., Tallón-Ballesteros, A.J., Zhang, M. (eds.) IDEAL 2017. LNCS, vol. 10585, pp. 551–559. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68935-7_60

    Chapter  Google Scholar 

  17. Tuba, E., Capor-Hrosik, R., Alihodzic, A., Tuba, M.: Drone placement for optimal coverage by brain storm optimization algorithm. In: Abraham, A., Muhuri, P.K., Muda, A.K., Gandhi, N. (eds.) HIS 2017. AISC, vol. 734, pp. 167–176. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76351-4_17

    Chapter  Google Scholar 

  18. Dolicanin, E., Fetahovic, I., Tuba, E., Capor-Hrosik, R., Tuba, M.: Unmanned combat aerial vehicle path planning by brain storm optimization algorithm. Stud. Inform. Control 27, 15–24 (2018)

    Article  Google Scholar 

  19. Yang, X.S.: A new metaheuristic bat-inspired algorithm. Stud. Computat. Intell. 284, 65–C74 (2010)

    MATH  Google Scholar 

  20. Tuba, E., Tuba, M., Simian, D.: Adjusted bat algorithm for tuning of support vector machine parameters. In: IEEE Congress on Evolutionary Computation (CEC), pp. 2225–2232. IEEE (2016)

    Google Scholar 

  21. Alihodzic, A., Tuba, E., Tuba, M.: An upgraded bat algorithm for tuning extreme learning machines for data classification. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 125–126. ACM (2017)

    Google Scholar 

  22. Alizadeh, B., Bakhteh, S.: A modified firefly algorithm for general inverse p-median location problems under different distance norms. OPSEARCH 54, 618–636 (2017)

    Article  MathSciNet  Google Scholar 

  23. Jayalakshmi, B., Singh, A.: A hybrid artificial bee colony algorithm for the p-median problem with positive/negative weights. OPSEARCH 54, 67–93 (2017)

    Article  MathSciNet  Google Scholar 

  24. Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010. LNCS, vol. 6145, pp. 355–364. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13495-1_44

    Chapter  Google Scholar 

  25. Li, J., Tan, Y.: The bare bones fireworks algorithm: a minimalist global optimizer. Appl. Soft Comput. 62, 454–462 (2018)

    Article  Google Scholar 

  26. Zheng, S., Janecek, A., Tan, Y.: Enhanced fireworks algorithm. In: IEEE Congress on Evolutionary Computation, pp. 2069–2077. IEEE (2013)

    Google Scholar 

  27. Zheng, S., Janecek, A., Li, J., Tan, Y.: Dynamic search in fireworks algorithm. In: IEEE Congress on Evolutionary Computation (CEC), pp. 3222–3229. IEEE (2014)

    Google Scholar 

  28. Li, J., Zheng, S., Tan, Y.: Adaptive fireworks algorithm. In: 2014 IEEE Congress on Evolutionary Computation, pp. 3214–3221. IEEE (2014)

    Google Scholar 

  29. Yu, C., Tan, Y.: Fireworks algorithm with covariance mutation. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1250–1256. IEEE (2015)

    Google Scholar 

  30. Zheng, S., Li, J., Janecek, A., Tan, Y.: A cooperative framework for fireworks algorithm. IEEE/ACM Trans. Comput. Biol. Bioinform. 14, 27–41 (2015)

    Article  Google Scholar 

  31. Li, J., Zheng, S., Tan, Y.: The effect of information utilization: Introducing a novel guiding spark in the fireworks algorithm. IEEE Trans. Evol. Comput. 21, 153–166 (2017)

    Article  Google Scholar 

  32. Bacanin, N., Tuba, M.: Fireworks algorithm applied to constrained portfolio optimization problem. In: IEEE Congress on Evolutionary Computation (CEC 2015), pp. 1242–1249 (2015)

    Google Scholar 

  33. He, W., Mi, G., Tan, Y.: Parameter optimization of local-concentration model for spam detection by using fireworks algorithm. In: Tan, Y., Shi, Y., Mo, H. (eds.) ICSI 2013. LNCS, vol. 7928, pp. 439–450. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38703-6_52

    Chapter  Google Scholar 

  34. Arsic, A., Tuba, M., Jordanski, M.: Fireworks algorithm applied to wireless sensor networks localization problem. In: IEEE Congress on Evolutionary Computation (CEC), pp. 4038–4044. IEEE (2016)

    Google Scholar 

  35. Tuba, M., Bacanin, N., Alihodzic, A.: Multilevel image thresholding by fireworks algorithm. In: Proceedings of the 25th International Conference Radioelektronika, pp. 326–330 (2015)

    Google Scholar 

  36. Tan, Y.: Implementation of fireworks algorithm based on GPU. In: Tan, Y. (ed.) Fireworks Algorithm, pp. 227–243. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46353-6_14

    Chapter  Google Scholar 

  37. Tuba, E., Tuba, M., Dolicanin, E.: Adjusted fireworks algorithm applied to retinal image registration. Stud. Inform. Control 26, 33–42 (2017)

    Article  Google Scholar 

  38. Tuba, E., Tuba, M., Simian, D., Jovanovic, R.: JPEG quantization table optimization by guided fireworks algorithm. In: Brimkov, V.E., Barneva, R.P. (eds.) IWCIA 2017. LNCS, vol. 10256, pp. 294–307. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59108-7_23

    Chapter  Google Scholar 

  39. Rabie, H.M., El-Khodary, I.A., Tharwat, A.A.: Particle swarm optimization algorithm for the continuous p-median location problems. In: 10th International Computer Engineering Conference (ICENCO), pp. 81–86. IEEE (2014)

    Google Scholar 

  40. Herda, M.: Parallel genetic algorithm for capacitated p-median problem. Procedia Eng. 192, 313–317 (2017)

    Article  Google Scholar 

  41. Lorena, L.A., Senne, E.L.: A column generation approach to capacitated p-median problems. Comput. Oper. Res. 31, 863–876 (2004)

    Article  MathSciNet  Google Scholar 

  42. Herda, M.: Combined genetic algorithm for capacitated p-median problem. In: 16th IEEE International Symposium on Computational Intelligence and Informatics (CINTI), pp. 151–154. IEEE (2015)

    Google Scholar 

  43. Beasley, J.E.: OR-library: distributing test problems by electronic mail. J. Oper. Res. Soc. 41, 1069–1072 (1990)

    Article  Google Scholar 

Download references

Acknowledgment

This research is supported by the Ministry of Education, Science and Technological Development of Republic of Serbia, Grant No. III-44006.

Author information

Authors and Affiliations

  1. Faculty of Informatics and Computing, Singidunum University, Belgrade, Serbia

    Eva Tuba, Ivana Strumberger & Nebojsa Bacanin

  2. Department of Mathematical Sciences, State University of Novi Pazar, Novi Pazar, Serbia

    Milan Tuba

Authors
  1. Eva Tuba
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ivana Strumberger
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nebojsa Bacanin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Milan Tuba
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eva Tuba .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

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

    Yuhui Shi

  3. Tongji University, Shanghai, China

    Qirong Tang

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tuba, E., Strumberger, I., Bacanin, N., Tuba, M. (2018). Bare Bones Fireworks Algorithm for Capacitated p-Median Problem. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10941. Springer, Cham. https://doi.org/10.1007/978-3-319-93815-8_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-93815-8_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93814-1

  • Online ISBN: 978-3-319-93815-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation