Skip to main content

Multi-swarm Optimization Algorithm Based on Firefly and Particle Swarm Optimization Techniques

  • Conference paper
  • First Online:
Artificial Intelligence and Soft Computing (ICAISC 2018)


In this paper, the two hybrid swarm-based metaheuristic algorithms are tested and compared. The first hybrid is already existing Firefly Particle Swarm Optimization (FFPSO), which is based, as the name suggests, on Firefly Algorithm (FA) and Particle Swarm Optimization (PSO). The secondly proposed hybrid is an algorithm using the multi-swarm method to merge FA and PSO. The performance of our developed algorithm is tested and compared with the FFPSO and canonical FA. Comparisons have been conducted on five selected benchmark functions, and the results have been evaluated for statistical significance using Friedman rank test.

T. Kadavy—This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014), further by the European Regional Development Fund under the Project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089 and by Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2018/003. This work is also based upon support by COST (European Cooperation in Science & Technology) under Action CA15140, Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO), and Action IC1406, High-Performance Modelling and Simulation for Big Data Applications (cHiPSet). The work was further supported by resources of A.I.Lab at the Faculty of Applied Informatics, Tomas Bata University in Zlin (

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Similar content being viewed by others


  1. Pluhacek, M., Senkerik, R., Viktorin, A., Kadavy, T., Zelinka, I.: A review of real-world applications of particle swarm optimization algorithm. In: Duy, V., Dao, T., Zelinka, I., Kim, S., Phuong, T. (eds.) AETA 2017. LNEE. Springer, Cham (2018).

    Chapter  Google Scholar 

  2. Du, W., Li, B.: Multi-strategy ensemble particle swarm optimization for dynamic optimization. Inf. Sci. 178(15), 3096–3109 (2008)

    Article  Google Scholar 

  3. Wang, H., Wu, Z., Rahnamayan, S., Sun, H., Liu, Y., Pan, J.: Multi-strategy ensemble artificial bee colony algorithm. Inf. Sci. 20(279), 587–603 (2014)

    Article  MathSciNet  Google Scholar 

  4. Blackwell, T., Branke, J.: Multi-swarm optimization in dynamic environments. In: Raidl, G.R., et al. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 489–500. Springer, Heidelberg (2004).

    Chapter  Google Scholar 

  5. Liang, J.J., Suganthan, P.N.: Dynamic multi-swarm particle swarm optimizer with local search. IEEE (2005)

    Google Scholar 

  6. Lynn, N., Suganthan, P.N.: Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm Evol. Comput. 1(24), 11–24 (2015)

    Article  Google Scholar 

  7. Nepomuceno, F.V., Engelbrecht, A.P.: A self-adaptive heterogeneous PSO for real-parameter optimization. IEEE (2013)

    Google Scholar 

  8. Zhan, Z.-H., Zhang, J., Li, Y., Shi, Y.-H.: Orthogonal learning particle swarm optimization. TEVC 15(6), 832–847 (2011)

    Google Scholar 

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

    Google Scholar 

  10. Allahverdi, A., Al-Anzi, F.S.: A PSO and a Tabu search heuristics for the assembly scheduling problem of the two-stage distributed database application. Comput. Oper. Res. 33(4), 1056–1080 (2006)

    Article  Google Scholar 

  11. Assareh, E., Behrang, M.A., Assari, M.R., Ghanbarzadeh, A.: Application of PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) techniques on demand estimation of oil in Iran. Energy 35(12), 5223–5229 (2010)

    Article  Google Scholar 

  12. Rudek, M., Canciglieri Jr., O., Greboge, T.: A PSO application in skull prosthesis modelling by superellipse. ELCVIA Electron. Lett. Comput. Vis. Image Anal. 12(2), 1–12 (2013).

    Article  Google Scholar 

  13. Yang, X.-S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Frome (2010)

    Google Scholar 

  14. Gandomi, A.H., Yang, X.S., Talatahari, S., Alavi, A.H.: Firefly algorithm with chaos. Commun. Nonlinear Sci. Numer. Simul. 18(1), 89–98 (2013)

    Article  MathSciNet  Google Scholar 

  15. Yang, X.: Firefly algorithm, Lévy flights and global optimization. In: Bramer, M., Ellis, R., Petridis, M. (eds.) Research and Development in Intelligent Systems XXVI, pp. 209–218. Springer, London (2010).

    Chapter  Google Scholar 

  16. Farahani, S.M., Abshouri, A.A., Nasiri, B., Meybodi, M.R.: A Gaussian firefly algorithm. Int. J. Mach. Learn. Comput. 1(5), 448 (2011)

    Article  Google Scholar 

  17. Kora, P., Rama Krishna, K.S.: Hybrid firefly and particle swarm optimization algorithm for the detection of bundle branch block. Int. J. Cardiovasc. Acad. 2(1), 44–48 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Tomas Kadavy .

Editor information

Editors and Affiliations

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

Kadavy, T., Pluhacek, M., Viktorin, A., Senkerik, R. (2018). Multi-swarm Optimization Algorithm Based on Firefly and Particle Swarm Optimization Techniques. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91252-3

  • Online ISBN: 978-3-319-91253-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics