Comparing Border Strategies for Roaming Particles on Single and Multi-swarm PSO

  • Tomas KadavyEmail author
  • Michal Pluhacek
  • Adam Viktorin
  • Roman Senkerik
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 573)


In this paper, the methods for handling particles that violate available search spaces are compared using a single and multi-swarm technique. The methods are soft borders and hypersphere universe. The goal is to compare this approaches and its combination. The comparisons are made on CEC’17 benchmark set functions. The experiments were carried out according to CEC benchmark rules and statistically evaluated.


Particle swarm optimization PSO Search space boundaries Multi-swarm Roaming particles 



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.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks 1995, pp. 1942–1948 (1995)Google Scholar
  2. 2.
    Awad, N.H. et al.: Problem definitions and evaluation criteria for CEC 2017 special session and competition on single-objective real-parameter numerical optimization (2016)Google Scholar
  3. 3.
    Pluhacek, M., Senkerik, R., Viktorin, A., Zelinka, I.: Single swarm and simple multi-swarm PSO comparison. In: 2016 9th EUROSIM Congress on Modelling and Simulation, Oulu, pp. 498–502 (2016)Google Scholar
  4. 4.
    Kennedy, J.: The particle swarm: social adaptation of knowledge. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 303–308 (1997)Google Scholar
  5. 5.
    Eberhart, R.C., Shi, A.Y.: Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation, CEC 2000, pp. 84–88. IEEE (2000)Google Scholar
  6. 6.
    Friedman, M.: The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32, 675–701 (1937)CrossRefzbMATHGoogle Scholar
  7. 7.
    Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

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

Personalised recommendations