Proposal of a New Swarm Optimization Method Inspired in Bison Behavior

  • Anezka Kazikova
  • Michal Pluhacek
  • Roman Senkerik
  • Adam Viktorin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 837)


This paper proposes a new swarm optimization algorithm inspired by bison behavior. The algorithm mimics two survival mechanisms of the bison herds: swarming into the circle of the strongest individuals and exploring the search space via organized run throughout the optimization process. The proposed algorithm is compared to the Particle Swarm Optimization and the Cuckoo Search algorithms on four benchmark functions.


Bison algorithm Bison Optimization Swarm intelligence 



This work was supported by Grant Agency of the Czech Republic – GACR P103/15/06700S, further by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014. Also 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/2017/004.


  1. 1.
    Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufmann Publishers, Burlington (2001)Google Scholar
  2. 2.
    Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press, Frome (2010)Google Scholar
  3. 3.
    Fister, I., Fister Jr., I., Yang, X.S., Brest, J.: A comprehensive review of firefly algorithms. Swarm Evol. Comput. 13, 34–46 (2013)CrossRefGoogle Scholar
  4. 4.
    Mirjalili, S., Mirjalili, S.M., Lewis, A.: Gray wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRefGoogle Scholar
  5. 5.
    Rajasekhar, A., Lynn, N., Das, S., Suganthan, P.N.: Computing with the collective intelligence of honey bees–a survey. Swarm Evol. Comput. 32, 25–48 (2017)CrossRefGoogle Scholar
  6. 6.
    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 (1995)Google Scholar
  7. 7.
    Odili, J.B., Kahar, M.N.M.: African buffalo optimization. Int. J. Softw. Eng. Comput. Syst. 2(1), 28–50 (2016)CrossRefGoogle Scholar
  8. 8.
    Berman, R.: American Bison (Nature Watch). Lerner Publications, Minneapolis (2008)Google Scholar
  9. 9.
    Zelinka, I.: SOMA—self-organizing migrating algorithm. In: New Optimization Techniques in Engineering, pp. 167–217. Springer, Heidelberg (2004)Google Scholar
  10. 10.
    Zelinka, I.: SOMA—self-organizing migrating algorithm. In: Self-Organizing Migrating Algorithm, pp. 3–49. Springer, Heidelberg (2016)Google Scholar
  11. 11.
    Faris, H.: EvoloPy GitHub repository (2017). Accessed 1 May 2017

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Anezka Kazikova
    • 1
  • Michal Pluhacek
    • 1
  • Roman Senkerik
    • 1
  • Adam Viktorin
    • 1
  1. 1.Faculty of Applied InformaticsTomas Bata University in ZlinZlinCzech Republic

Personalised recommendations