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Effect of Particle Initialization on the Performance of Particle Swarm Niching Algorithms

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Swarm Intelligence (ANTS 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6234))

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Abstract

The vector-based PSO (VBPSO) [3,4,5] was developed to locate multiple solutions to multi-modal optimization problems. Three versions of the VBPSO were published, and shown to be very efficient in locating mutliple optima. This is despite the fact that the VBPSO algorithms initialize particles using standard pseudo random number generators. The main objective of this article is to show that the perfomance of the VBPSO algorithms can be improved by initializing particles using Sobol sequences [1,2].

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References

  1. Bratley, P., Fox, B.: Algorithm 659: Implementing Sobol’s Quasirandom Sequence Generator. ACM Trans. on Math. Softw. 14, 88–100 (1988)

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  2. Joe, S., Kuo, F.: Remark on Algorithm 659: Implementing Sobol’s Quasirandom Sequence Generator. ACM Trans. on Math. Softw. 29, 49–57 (2003)

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  3. Schoeman, I., Engelbrecht, A.: Using Vector Operations to Identify Niches for Particle Swarm Optimization. In: Proceedings of the IEEE Conference on Cybernetics and Intelligent Systems, pp. 361–366 (2004)

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  4. Schoeman, I., Engelbrecht, A.: A Parallel Vector-Based Particle Swarm Optimiser. In: Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 268–271 (2005)

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  5. Schoeman, I., Engelbrecht, A.: Containing Particles Inside Niches when Optimising using Multimodal Functions. In: Proceedings of SAICSIT, pp. 78–85 (2005)

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Pretoria, South Africa

    Isabella Schoeman & Andries P. Engelbrecht

Authors
  1. Isabella Schoeman
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  2. Andries P. Engelbrecht
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Editor information

Editors and Affiliations

  1. IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. IRIDIA, CoDE, Université Libre de Bruxelles, Av. F. Roosevelt 50, CP 194/6, 1050, Brussels, Belgium

    Mauro Birattari

  3. ’Dalla Molle’ Institute for Artificial Intelligence (IDSIA), Galleria 2, 6928, Manno-Lugano, Switzerland

    Gianni A. Di Caro

  4. Complex Systems Institute, Paris-Ile-de-France, CREA, Ecole Polytechnique & CNRS, 57-59, rue Lhomond, 75005, Paris, France

    René Doursat

  5. Department of Computer Science, University of Pretoria, 0002, P.O. Box, Pretoria, South Africa

    Andries P. Engelbrecht

  6. EPFL, Laboratory of Intelligent Systems, Lausanne, Switzerland

    Dario Floreano

  7. Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), Lugano, Switzerland

    Luca Maria Gambardella

  8. Department of Automatic Control and Systems Engineering, The University of Sheffield, Mappin Street, S1 3JD, Sheffield, UK

    Roderich Groß

  9. KOVAN Research Lab., Department of Computer Engineering, Middle East Technical University, Inonu Bulvari, 06531, Ankara, Turkey

    Erol Åžahin

  10. Department of Bioengineering, Binghamton University, State University of New York, 13902-6000, Binghamton NY, USA

    Hiroki Sayama

  11. IRIDIA, CoDE, Université Libre de Bruxelles, Avenue F. Roosevelt 50, CP 194/6, 1050, Brussels, Belgium

    Thomas Stützle

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© 2010 Springer-Verlag Berlin Heidelberg

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Schoeman, I., Engelbrecht, A.P. (2010). Effect of Particle Initialization on the Performance of Particle Swarm Niching Algorithms. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2010. Lecture Notes in Computer Science, vol 6234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15461-4_60

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  • DOI: https://doi.org/10.1007/978-3-642-15461-4_60

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15460-7

  • Online ISBN: 978-3-642-15461-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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