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Comparing Strategies for Search Space Boundaries Violation in PSO

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10246))

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Abstract

In this paper, we choose to compare four methods for controlling particle position when it violates the search space boundaries and the impact on the performance of Particle Swarm Optimization algorithm (PSO). The methods are: hard borders, soft borders, random position and spherical universe. The goal is to compare the performance of these methods for the classical version of PSO and popular modification – the Attractive and Repulsive Particle Swarm Optimization (ARPSO). The experiments were carried out according to CEC benchmark rules and statistically evaluated.

T. Kadavy—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.

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References

  1. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)

    Google Scholar 

  2. Nepomuceno, F.V., Engelbrecht, A.P.: A self-adaptive heterogeneous PSO for real-parameter optimization. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 361–368. IEEE (2013)

    Google Scholar 

  3. Zhan, Z.-H., et al.: Orthogonal learning particle swarm optimization. IEEE Trans. Evol. Comput. 15(6), 832–847 (2011)

    Article  Google Scholar 

  4. Riget, J., Vesterstrom, J.S.: A diversity-guided particle swarm optimizer-the ARPSO. Department of Computer Science, University of Aarhus, Aarhus, Denmark, Technical report 2002–02 (2002)

    Google Scholar 

  5. Chen, Q., et al.: Problem definitions and evaluation criteria for CEC 2015 special session on bound constrained single-objective computationally expensive numerical optimization

    Google Scholar 

  6. 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 

  7. Eberhart, R.C., Shi, 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 

  8. 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)

    Article  MATH  Google Scholar 

  9. Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlin, T.G. Masaryka 5555, 760 01, Zlin, Czech Republic

    Tomas Kadavy, Michal Pluhacek, Adam Viktorin & Roman Senkerik

Authors
  1. Tomas Kadavy
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  2. Michal Pluhacek
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  3. Adam Viktorin
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  4. Roman Senkerik
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Corresponding author

Correspondence to Tomas Kadavy .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  4. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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Kadavy, T., Pluhacek, M., Viktorin, A., Senkerik, R. (2017). Comparing Strategies for Search Space Boundaries Violation in PSO. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10246. Springer, Cham. https://doi.org/10.1007/978-3-319-59060-8_59

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  • DOI: https://doi.org/10.1007/978-3-319-59060-8_59

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59059-2

  • Online ISBN: 978-3-319-59060-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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