Skip to main content
SpringerLink
Log in
Book cover

International Conference on Swarm Intelligence

ANTS 2012: Swarm Intelligence pp 188–195Cite as

A Self-adaptive Heterogeneous PSO Inspired by Ants

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7461))

Abstract

Heterogeneous particle swarm optimizers have been proposed where particles are allowed to implement different behaviors. A selected behavior may not be optimal for the duration of the search process. Since the optimality of a behavior depends on the fitness landscape it is necessary that particles be able to dynamically adapt their behaviors. This paper introduces two new self-adaptive heterogeneous particle swarm optimizers which are influenced by the ant colony optimization meta-heuristic. These self-adaptive strategies are compared with three other heterogeneous particle swarm optimizers. The results show that the proposed models outrank the existing models overall.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Blackwell, T., Branke, J.: Multi-swarm Optimization in Dynamic Environments. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 489–500. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  2. Clerc, M., Kennedy, J.: The Particle Swarm - Explosion, Stability, and Convergence in a Multidimensional Complex Space. IEEE Transactions on Evolutionary Computation, 58–73 (2002)

    Google Scholar 

  3. Cooren, Y., Clerc, M., Siarry, P.: Performance Evaluation of TRIBES, an Adaptive Particle Swarm Optimization Algorithm. Swarm Intelligence 3, 149–178 (2009)

    Article  Google Scholar 

  4. Demšar, J.: Statistical Comparisons of Classifiers Over Multiple Data Sets. The Journal of Machine Learning Research, 1–30 (2006)

    Google Scholar 

  5. Dorigo, M.: Optimization, Learning and Natural Algorithms (in Italian). Ph.D. thesis, Dipartimento di Elettronica, Politecnico di Milano, Milan, Italy (1992)

    Google Scholar 

  6. Eberhart, R., Kennedy, J.: A New Optimizer using Particle Swarm Theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43 (1995)

    Google Scholar 

  7. Eberhart, R., Shi, Y.: Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 84–88 (2000)

    Google Scholar 

  8. Engelbrecht, A.P.: Heterogeneous Particle Swarm Optimization. In: Dorigo, M., Birattari, M., Di Caro, G.A., Doursat, R., Engelbrecht, A.P., Floreano, D., Gambardella, L.M., Groß, R., Şahin, E., Sayama, H., Stützle, T. (eds.) ANTS 2010. LNCS, vol. 6234, pp. 191–202. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  9. Kennedy, J.: The Particle Swarm: Social Adaptation of Knowledge. In: Proceedings of the IEEE International Congress on Evolutionary Computation, pp. 303–308 (1997)

    Google Scholar 

  10. Kennedy, J.: Bare Bones Particle Swarms. In: Proceedings of the IEEE Swarm Intelligence Symposium, pp. 80–87 (2003)

    Google Scholar 

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

    Google Scholar 

  12. Leonard, B., Engelbrecht, A., van Wyk, A.: Heterogeneous Particle Swarms in Dynamic Environments. In: Proceedings of the IEEE Swarm Intelligence Symposium, pp. 1–8 (2011)

    Google Scholar 

  13. Li, C., Yang, S.: Adaptive Learning Particle Swarm Optimizer-II for Global Optimization. In: Proceedings of the Congress on Evolutionary Computation, pp. 1–8 (2010)

    Google Scholar 

  14. Mendes, R., Kennedy, J., Neves, J.: The Fully Informed Particle Swarm: Simpler, Maybe Better. IEEE Transactions on Evolutionary Computation, 204–210 (2004)

    Google Scholar 

  15. Montes de Oca, M., Peña, J., Stützle, T., Pinciroli, C., Dorigo, M.: Heterogeneous Particle Swarm Optimizers. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 698–705 (2009)

    Google Scholar 

  16. Ratnaweera, A., Halgamuge, S., Watson, H.: Self-Organizing Hierarchical Particle Swarm Optimizer with Time-Varying Acceleration Coefficients. IEEE Transactions on Evolutionary Computation, 240–255 (2004)

    Google Scholar 

  17. Shi, Y., Eberhart, R.: A Modified Particle Swarm Optimizer. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 69–73 (1998)

    Google Scholar 

  18. Spanevello, P., Montes de Oca, M.: Experiments on Adaptive Heterogeneous PSO Algorithms. In: Proceedings of the Doctoral Symposium on Engineering Stochastic Local Search Algorithms, pp. 36–40 (2009)

    Google Scholar 

  19. Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.P., Auger, A., Tiwari, S.: Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real Parameter Optimization. Tech. rep., Nanyang Technological University (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Filipe V. Nepomuceno & Andries P. Engelbrecht

Authors
  1. Filipe V. Nepomuceno
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andries P. Engelbrecht
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

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

    Marco Dorigo , Mauro Birattari  & Thomas Stützle ,  & 

  2. ALBCOM, Department Llenguatges i Sistemes Informatics, Universitat Politecnica de Catalunya, Jordi Girona 1-3, Omega 112 Campus Nord, 08034, Barcelona, Spain

    Christian Blum

  3. Instituto de Telecomunicações & Instituto Universitário de Lisboa (ISCTE-IUL), Av. das Foras Armadas, 1649-026, Lisboa, Portugal

    Anders Lyhne Christensen

  4. Department of Computer Science, University of Pretoria, 0002, Pretoria, South Africa

    Andries P. Engelbrecht

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

    Roderich Groß

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Nepomuceno, F.V., Engelbrecht, A.P. (2012). A Self-adaptive Heterogeneous PSO Inspired by Ants. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2012. Lecture Notes in Computer Science, vol 7461. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32650-9_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-32650-9_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32649-3

  • Online ISBN: 978-3-642-32650-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation