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Workshops on Applications of Evolutionary Computation

EvoWorkshops 2004: Applications of Evolutionary Computing pp 513–524Cite as

A Hierarchical Particle Swarm Optimizer for Dynamic Optimization Problems

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3005))

Abstract

Particle Swarm Optimization (PSO) methods for dynamic function optimization are studied in this paper. We compare dynamic variants of standard PSO and Hierarchical PSO (H-PSO) on different dynamic benchmark functions. Moreover, a new type of hierarchical PSO, called Partitioned H-PSO (PH-PSO), is proposed. In this algorithm the hierarchy is partitioned into several sub-swarms for a limited number of generations after a change occurred. Different methods for determining the time when to rejoin the hierarchy and how to handle the topmost sub-swarm are discussed. The test results show that H-PSO performs significantly better than PSO on all test functions and that the PH-PSO algorithms often perform best on multimodal functions where changes are not too severe.

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Author information

Authors and Affiliations

  1. Parallel Computing and Complex Systems Group Department of Computer Science, University of Leipzig, Augustusplatz 10/11, D-04109, Leipzig, Germany

    Stefan Janson & Martin Middendorf

Authors
  1. Stefan Janson
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  2. Martin Middendorf
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Editor information

Editors and Affiliations

  1. Institute of Computer Graphics and Algorithms, Vienna University of Technology, Favoritenstraße 9–11/1861, 1040, Vienna, Austria

    Günther R. Raidl

  2. Dipartimento di Ingegneria dell’Informazione, Università di Parma,  

    Stefano Cagnoni

  3. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  4. School of Mathematical and Computer Science, Heriot-Watt University, EH14 8AS, Edinburgh, UK

    David Wolfe Corne

  5. Institute of Computer Science, University of Bremen, 28359, Bremen, Germany

    Rolf Drechsler

  6. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

  7. Computing Laboratory, University of Kent, Canterbury, UK

    Colin G. Johnson

  8. CISUC, Department of Informatics Engineering, University of Coimbra, Polo II of the University of Coimbra, 3030, Coimbra, Portugal

    Penousal Machado

  9. ICIS, Radboud University Nijmegen, The Netherlands

    Elena Marchiori

  10. Johannes Gutenberg University, Mainz, Germany

    Franz Rothlauf

  11. School of Computing Sciences, UEA Norwich, University of East Anglia, NR4 7TJ, Norwich, UK

    George D. Smith

  12. Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy

    Giovanni Squillero

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© 2004 Springer-Verlag Berlin Heidelberg

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Janson, S., Middendorf, M. (2004). A Hierarchical Particle Swarm Optimizer for Dynamic Optimization Problems. In: Raidl, G.R., et al. Applications of Evolutionary Computing. EvoWorkshops 2004. Lecture Notes in Computer Science, vol 3005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24653-4_52

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  • DOI: https://doi.org/10.1007/978-3-540-24653-4_52

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21378-9

  • Online ISBN: 978-3-540-24653-4

  • eBook Packages: Springer Book Archive

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