Skip to main content
SpringerLink
Log in

Diversity Rate of Change Measurement for Particle Swarm Optimisers

  • Conference paper
Swarm Intelligence (ANTS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8667))

Included in the following conference series:

Abstract

The diversity of a particle swarm can reflect the swarm’s explorative/exploitative behaviour at a given time step. This paper proposes a diversity rate of change measure to quantify the rate at which particle swarms decrease their diversity over time. The proposed measure is based on a two-piecewise linear approximation of diversity measurements sampled at regular time steps. The proposed measure is the slope of the first of the two lines. It is shown that, when comparing the measure among different algorithms, the measure reflects the differences in the behaviour of algorithms in terms of their exploration-exploitation trade-off. The measure can potentially be used to characterise and classify different algorithms based on algorithm behaviour.

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

Access this chapter

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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Van den Bergh, F., Engelbrecht, A.P.: A new locally convergent particle swarm optimizer. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, pp. 96–101 (2002)

    Google Scholar 

  2. Van den Bergh, F., Engelbrecht, A.P.: A study of particle swarm optimization particle trajectories. Information Sciences 176(8), 937–971 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  3. Chen, M.R., Li, X., Zhang, X., Lu, Y.Z.: A novel particle swarm optimizer hybridized with extremal optimization. Applied Soft Computing 10(2), 367–373 (2010)

    Article  Google Scholar 

  4. De Jong, K.A.: Analysis of the behavior of a class of genetic adaptive systems. Ph.D. thesis, University of Michigan, Ann Arbor, MI, USA (1975)

    Google Scholar 

  5. Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, New York, NY, vol. 1, pp. 39–43 (1995)

    Google Scholar 

  6. Engelbrecht, A.P.: Computational intelligence: an introduction. John Wiley & Sons (2007)

    Google Scholar 

  7. Engelbrecht, A.P.: Scalability of a heterogeneous particle swarm optimizer. In: Proceedings of the 2011 IEEE Symposium on Swarm Intelligence, pp. 1–8. IEEE (2011)

    Google Scholar 

  8. Fan, S.K.S., Chang, J.M.: Dynamic multi-swarm particle swarm optimizer using parallel PC cluster systems for global optimization of large-scale multimodal functions. Engineering Optimization 42(5), 431–451 (2010)

    Article  Google Scholar 

  9. Kennedy, J.: Bare bones particle swarms. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium, pp. 80–87. IEEE (2003)

    Google Scholar 

  10. Kennedy, J., Eberhart, R., et al.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  11. Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Proceedings of the 2002 IEEE World Congress on Computational Intelligence, vol. 2, pp. 1671–1676. IEEE Computer Society (2002)

    Google Scholar 

  12. Kennedy, J.F., Kennedy, J., Eberhart, R.C.: Swarm intelligence. Morgan Kaufmann (2001)

    Google Scholar 

  13. Mishra, S.: Some new test functions for global optimization and performance of repulsive particle swarm method. Tech. rep., University Library of Munich, Germany (2006)

    Google Scholar 

  14. Monson, C.K., Seppi, K.D.: Adaptive diversity in PSO. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 59–66. ACM (2006)

    Google Scholar 

  15. Olorunda, O., Engelbrecht, A.P.: Measuring exploration/exploitation in particle swarms using swarm diversity. In: Proceedings of the 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 1128–1134. IEEE (2008)

    Google Scholar 

  16. Peer, E.S., Van den Bergh, F., Engelbrecht, A.P.: Using neighbourhoods with the guaranteed convergence PSO. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium, pp. 235–242. IEEE (2003)

    Google Scholar 

  17. Price, K., Storn, R.M., Lampinen, J.A.: Appendix A.1: Unconstrained uni-modal test functions. In: Differential Evolution: a Practical Approach to Global Optimization. Natural Computing Series, pp. 514–533. Springer, Berlin (2006)

    Google Scholar 

  18. Rahnamayan, S., Tizhoosh, H.R., Salama, M.: A novel population initialization method for accelerating evolutionary algorithms. Computers & Mathematics with Applications 53(10), 1605–1614 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  19. Suganthan, P.N.: Particle swarm optimiser with neighbourhood operator. In: Proceedings of the 1999 Congress on Evolutionary Computation, vol. 3. IEEE (1999)

    Google Scholar 

  20. Tang, K., Yao, X., Suganthan, P.N., MacNish, C., Chen, Y.P., Chen, C.M., Yang, Z.: Benchmark functions for the CEC 2008 special session and competition on large scale global optimization. Tech. rep. (2007)

    Google Scholar 

  21. Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 3(2), 82–102 (1999)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Phlippie Bosman & Andries P. Engelbrecht

Authors
  1. Phlippie Bosman
    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, CoDE, Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo  & Thomas Stützle  & 

  2. IRIDIA, Université Libre de Bruxelles, Brussels, Belgium

    Mauro Birattari

  3. New Jersey Institute of Technology, Rutgers University, Newark, NJ, USA

    Simon Garnier

  4. Department of Computer Science, University of Paderborn, Zukunftsmeile 1, 33102, Paderborn, Germany

    Heiko Hamann

  5. Department of Mathematical Sciences, University of Delaware, Newark, DE, USA

    Marco Montes de Oca

  6. LIRIS, INSA de Lyon, 69621, Villeurbanne Cedex, France

    Christine Solnon

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Bosman, P., Engelbrecht, A.P. (2014). Diversity Rate of Change Measurement for Particle Swarm Optimisers. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2014. Lecture Notes in Computer Science, vol 8667. Springer, Cham. https://doi.org/10.1007/978-3-319-09952-1_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-09952-1_8

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09951-4

  • Online ISBN: 978-3-319-09952-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation