Introducing the Run Support Strategy for the Bison Algorithm

  • Anezka KazikovaEmail author
  • Michal Pluhacek
  • Tomas Kadavy
  • Roman Senkerik
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 554)


Many state-of-the-art optimization algorithms stand against the threat of premature convergence. While some metaheuristics try to avoid it by increasing the diversity in various ways, the Bison Algorithm faces this problem by guaranteeing stable exploitation – exploration ratio throughout the whole optimization process. Still, it is important to ensure, that the newly discovered solutions can affect the overall optimization process. In this paper, we propose a new Run Support Strategy for the Bison Algorithm, that should enhance the utilization of newly discovered solutions, and should be suitable for both continuous and discrete optimization.


Bison Algorithm Run Support Strategy Exploration optimization 



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/2019/002. 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 (


  1. 1.
    Back, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, Oxford (1996)zbMATHGoogle Scholar
  2. 2.
    Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3(2), 95–99 (1988)CrossRefGoogle Scholar
  3. 3.
    Chakraborty, A., Kar, A.K.: Swarm intelligence: a review of algorithms. In: Nature-Inspired Computing and Optimization, pp. 475–494. Springer (2017)Google Scholar
  4. 4.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4 (1995)Google Scholar
  5. 5.
    Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Software. 69, 46–61 (2014)CrossRefGoogle Scholar
  6. 6.
    Zelinka, I.: SOMA—self-organizing migrating algorithm. In: New Optimization Techniques in Engineering, pp. 167–217. Springer, Heidelberg (2004)Google Scholar
  7. 7.
    Yang, X.-S., Deb, S.: Cuckoo search via Levy flights. In: Proceedings of World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), December 2009, India, pp. 210–214. IEEE Publications, USA (2009)Google Scholar
  8. 8.
    Pluhacek, M., Senkerik, R., Viktorin, A., Kadavy, T., Zelinka, I.: A review of real-world applications of particle swarm optimization algorithm. In: Lecture Notes in Electrical Engineering, pp. 115–122. Springer, Cham (2018). ISSN 1876-1100Google Scholar
  9. 9.
    Mohamad, A., Zain, A.M., Bazin, N.E.N., Udin, A.: Cuckoo search algorithm for optimization problems-a literature review. In: Applied Mechanics and Materials, vol. 421, pp. 502–506. Trans Tech Publications (2013)Google Scholar
  10. 10.
    Chen, J., Xin, B., Peng, Z., Dou, L., Zhang, J.: Optimal contraction theorem for exploration–exploitation tradeoff in search and optimization. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 39(3), 680–691 (2009)CrossRefGoogle Scholar
  11. 11.
    Pluhacek, M., Senkerik, R., Viktorin, A., Zelinka, I.: Chaos Enhanced Repulsive MC-PSO/DE Hybrid. Springer, Cham (2016)CrossRefGoogle Scholar
  12. 12.
    Riget, J., Vesterstrøm, J.S.: A diversity-guided particle swarm optimizer-the ARPSO. Dept. Comput. Sci., Univ. of Aarhus, Denmark, Technical report (2002)Google Scholar
  13. 13.
    Kazikova, A., Pluhacek, M., Viktorin, A., Senkerik, R.: Proposal of a new swarm optimization method inspired in bison behavior. In: Matousek, R. (ed.) Recent Advances in Soft Computing (MENDEL 2017), Advances in Intelligent Systems and Computing. Springer, Cham (2018)Google Scholar
  14. 14.
    Kazikova, A., Pluhacek, M., Senkerik, R.: Performance of the Bison Algorithm on benchmark IEEE CEC 2017. In: Silhavy, R. (ed.) Artificial Intelligence and Algorithms in Intelligent Systems, CSOC2018, Advances in Intelligent Systems and Computing, vol. 764. Springer, Cham (2018)Google Scholar
  15. 15.
    Kazikova, A., Pluhacek, M., Viktorin, A., Senkerik, R.: New Running Technique for the Bison Algorithm. 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 (2018)Google Scholar
  16. 16.
    Kazikova, A., Pluhacek, M., Senkerik, R.: Regarding the behavior of bison runners within the bison algorithm. In: Mendel Journal Series 2018, vol. 24, pp. 63–70 (2018)Google Scholar
  17. 17.
    Berman, R.: American Bison. Nature Watch. Lerner Publications, Minneapolis (2008)Google Scholar
  18. 18.
    Sörensen, K.: Metaheuristics—the metaphor exposed. Int. Trans. Oper. Res. 22(1), 3–18 (2015)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Kazikova, A., Pluhacek, M., Senkerik, R.: Tuning of The Bison Algorithm control parameters. In: 32nd European Conference on Modelling and Simulation, 22nd May–26th May. European Council for Modeling and Simulation (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Anezka Kazikova
    • 1
    Email author
  • Michal Pluhacek
    • 1
  • Tomas Kadavy
    • 1
  • Roman Senkerik
    • 1
  1. 1.Faculty of Applied InformaticsTomas Bata University in ZlinZlinCzech Republic

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