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Workshops on Applications of Evolutionary Computation

EvoWorkshops 2004: Applications of Evolutionary Computing pp 489–500Cite as

Multi-swarm Optimization in Dynamic Environments

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

Abstract

Many real-world problems are dynamic, requiring an optimization algorithm which is able to continuously track a changing optimum over time. In this paper, we present new variants of Particle Swarm Optimization (PSO) specifically designed to work well in dynamic environments. The main idea is to extend the single population PSO and Charged Particle Swarm Optimization (CPSO) methods by constructing interacting multi-swarms. In addition, a new algorithmic variant, which broadens the implicit atomic analogy of CPSO to a quantum model, is introduced. The multi-swarm algorithms are tested on a multi-modal dynamic function – the moving peaks benchmark – and results are compared to the single population approach of PSO and CPSO, and to results obtained by a state-of-the-art evolutionary algorithm, namely self-organizing scouts (SOS). We show that our multi-swarm optimizer significantly outperforms single population PSO on this problem, and that multi-quantum swarms are superior to multi-charged swarms and SOS.

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References

  1. Blackwell, T.M.: Swarms in Dynamic Environments. In: Proc Genetic and Evolutionary Computation Conference, pp. 1–12 (2003)

    Google Scholar 

  2. Blackwell, T.M., Bentley, P.J.: Dynamic search with charged swarms. In: Proc Genetic and Evolutionary Computation Conference, pp. 19–26 (2002)

    Google Scholar 

  3. Blackwell, T.M.: Particle Swarms and Population Diversity I: Analysis. In: GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, pp. 103–107 (2003)

    Google Scholar 

  4. Blackwell, T.M.: Particle Swarms and Population Diversity II: Experiments. In: GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, pp. 108– 112 (2003)

    Google Scholar 

  5. Blackwell, T.M.: Particle Swarms and Population Diversity. Soft Computing (submitted)

    Google Scholar 

  6. Blackwell, T.M.: Swarm Music: Improvised Music with Multi-Swarms. In: Proc. AISB 2003 Symposium on Artificial Intelligence and Creativity in Arts and Science, pp. 41–49 (2003)

    Google Scholar 

  7. Branke, J., Schmeck, H.: Designing Evolutionary Algorithms for Dynamic Optimization Problems. In: Tsutsui, S., Ghosh, A. (eds.) Theory and Application of Evolutionary Computation: Recent Trends, pp. 239–262. Springer, Heidelberg (2002)

    Google Scholar 

  8. Branke, J.: Memory Enhanced EA for Changing Optimization Problems. Congress on Evolutionary Computation CEC 1999 3, 1875–1882 (1999)

    Google Scholar 

  9. Branke, J.: Evolutionary optimization in dynamic environments. Kluwer, Dordrecht (2001)

    Google Scholar 

  10. Branke, J.: The moving peaks benchmark, website Online http://www.aifb.unikarlsruhe.de/~jbr/MovPeaks

  11. Brits, R., Engelbrecht, A.P., van den Bergh, F.: A Niching Particle Swarm Optimizer. In: Fourth Asia-Pacific Conference on Simulated Evolution and Learning, Singapore, pp. 692–696 (2002)

    Google Scholar 

  12. Carlisle, A., Dozier, G.: Adapting Particle Swarm Optimization to Dynamic Environments. In: Proc of Int Conference on Artificial Intelligence, pp. 429–434 (2000)

    Google Scholar 

  13. Clerc, M., Kennedy, J.: The Particle Swarm: Explosion, Stability and Convergence in a Multi-Dimensional Complex Space. IEEE Transactions on Evolutionary Computation 6, 158–173 (2002)

    Article  Google Scholar 

  14. French, A.P., Taylor, E.F.: An Introduction to Quantum Physics. W.W. Norton and Company, New York (1978)

    Google Scholar 

  15. Hu, X., Eberhart, R.C.: Adaptive particle swarm optimisation: detection and response to dynamic systems. In: Proc Congress on Evolutionary Computation, pp. 1666–1670 (2002)

    Google Scholar 

  16. Kennedy, J., ad Mendes, R.: Population Structure and Particle Swarm Performance. Congress on Evolutionary Computation, 1671–1676 (2002)

    Google Scholar 

  17. Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks. IV, pp. 1942–1948 (1995)

    Google Scholar 

  18. Parsopoulos, K.E., Vrahatis, M.N.: Recent Approaches to Global Optimization Problems through ParticleSwarm Optimization. Natural Computing 1, 235–306 (2002)

    Article  MATH  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computing, Goldsmiths College, University of London New Cross, London, SE14 6NW, U.K.

    Tim Blackwell

  2. Institute AIFB, University of Karlsruhe, D-76128, Karlsruhe, Germany

    Jürgen Branke

Authors
  1. Tim Blackwell
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  2. Jürgen Branke
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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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Blackwell, T., Branke, J. (2004). Multi-swarm Optimization in Dynamic Environments. 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_50

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  • DOI: https://doi.org/10.1007/978-3-540-24653-4_50

  • 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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