Nothing Special   »   [go: up one dir, main page]

Skip to main content

Gender-Hierarchy Particle Swarm Optimizer Based on Punishment

  • Conference paper
Advances in Swarm Intelligence (ICSI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6145))

Included in the following conference series:

Abstract

The paper presents a novel particle swarm optimizer (PSO), called gender-hierarchy particle swarm optimizer based on punishment (GH-PSO). In the proposed algorithm, the social part and recognition part of PSO both are modified in order to accelerate the convergence and improve the accuracy of the optimal solution. Especially, a novel recognition approach, called general recognition, is presented to furthermore improve the performance of PSO. Experimental results show that the proposed algorithm shows better behaviors as compared to the standard PSO, tribes-based PSO and GH-PSO with tribes.

The research is funded by National Natural Science Foundation of China (10926198).

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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.

Similar content being viewed by others

References

  1. Kennedy, J., Eberhart, R.C.: A new optimizer using particle swarm theory. In: Proc. 6th International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43 (1995)

    Google Scholar 

  2. Shi, Y.H., Eberhart, R.C.: Empirical study of particle swarm optimization. In: Proc. 1999 Congress on Evolutionary Computation, pp. 1945–1950. IEEE Press, Piscataway (1999)

    Google Scholar 

  3. Clerc, M., Kennedy, J.: The particle swarmexplosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6, 58–73 (2002)

    Article  Google Scholar 

  4. Chen, K., Li, T.H., Cao, T.C.: Tribe-PSO: A novel global optimization algorithm and its application in molecular docking. Chemometrics Intell. Lab. Syst. 82, 248–259 (2006)

    Article  Google Scholar 

  5. Cooren, Y., Clerc, M., Siarry, P.: Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm. Swarm Intelligence 3, 1935–3820 (2009)

    Article  Google Scholar 

  6. Shi, Y.H., Eberhart, R.C.: A modified particle swarm optimizer. In: Proc.1998 IEEE International Conference on Computational Intelligence, Anchorage, Alaska, pp. 69–73. IEEE Press, Los Alamitos (1998)

    Google Scholar 

  7. Trelea, I.C.: The particle swarm optimization algrorithm:convergence analysis and parameter selection. Inform. Proc. Lett. 85, 317–325 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  8. Fan, H.Y.: A modification to particle swarm optimization algorithm. Eng. Comput. 19, 970–989 (2002)

    Article  MATH  Google Scholar 

  9. Schutte, J.F., Groenwold, A.A.: A study of global optimization using particle swarms. Struct. Multidis. Optim. 25, 261–269 (2003)

    Article  Google Scholar 

  10. Braendler, D., Hendtlass, T.: Improving particle swarm optimization using the collective movement of the swarm. IEEE Trans. Evol. Comput. (to appear)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Gao, J., Li, H., Hu, L. (2010). Gender-Hierarchy Particle Swarm Optimizer Based on Punishment. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13495-1_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13495-1_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13494-4

  • Online ISBN: 978-3-642-13495-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics