Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Parallel quantum-behaved particle swarm optimization

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Quantum-behaved particle swarm optimization (QPSO), like other population-based algorithms, is intrinsically parallel. The master–slave (synchronous and asynchronous) and static subpopulation parallel QPSO models are investigated and applied to solve the inverse heat conduction problem of identifying the unknown boundary shape. The performance of all these parallel models is compared. The synchronous parallel QPSO can obtain better solutions, while the asynchronous parallel QPSO converges fast without idle waiting. The scalability of the static subpopulation parallel QPSO is not as good as the master–slave parallel model.

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

Access this article

Log in via an institution

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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Perth, Australia, pp 1942–1948

  2. Eberhart RC, Shi Y (1998) Comparison between genetic algorithm and particle swarm optimization, evolutionary programming VII, lecture notes in computer science 1447. Springer, Heidelberg, pp 611–616

    Google Scholar 

  3. Van den Bergh F (2001) An analysis of particle swarm optimizers, Ph.D. diss. University of Pretoria, South Africa

    Google Scholar 

  4. Sun J, Feng B, Xu WB (2004) Particle swarm optimization with particles having quantum behaviour. In: Proceedings of congress evolutionary computational, Portland, USA, pp 325–331

  5. Clerc M, Kennedy J (2002) The particle swarm: explosion, stability, and convergence in a multi-dimensional complex space. IEEE Trans Evol Comput 6:58–73

    Article  Google Scholar 

  6. Sun J, Xu WB, Liu J (2005) Parameter selection of quantum-behaved particle Swarm optimization. Lect Notes Comput Sci 3612:543–552

    Article  Google Scholar 

  7. Sun J, Xu WB, Feng B (2004) A global search strategy of quantum-behaved particle swarm optimization. In: Proceedings of IEEE conference on cybernetics and intelligent systems, Singapore, pp 111-116

  8. Sun J, Xu WB, Feng B (2005) Adaptive parameter control for quantum-behaved particle swarm optimization on individual level. In: Proceedings of IEEE International conference on systems, man and cybernetics, Hawaii, pp 3049–3054

  9. Nowostawski M, Poli R (1999) Parallel genetic algorithm taxonomy. In: Proceedings of third international conference knowledge-based intelligent information engineering systems, Adelaide, SA, Australia, pp 88–92

  10. Koh B, George AD, Haftka RT, Fregly BJ (2006) Parallel asynchronous particle swarm optimization. Int J Numer Meth Eng 67:578–595

    Article  MATH  Google Scholar 

  11. Carlisle A, Dozier G (2001) An off-the-shelf PSO. In: Proceeding of the workshop on particle swarm optimization, Indianapolis, USA

  12. Tian N, Lai CH, Pericleous K, Xu WB Inverse identification of boundary shape using a hybrid approach with boundary element method. Int J Comput Math (submitted)

  13. Tikhonov AN, Arsenin VY (1977) Solution of Ill-posed problems. Winston, Washington DC

    Google Scholar 

  14. Lin C.-M., Li M.-C., Ting A.-B., Lin M.-H. (2011) A robust self-learning PID control system design for nonlinear systems using a particle swarm optimization algorithm. Int J Mach Learn Cybern 2(4):225–234

    Article  Google Scholar 

  15. Rana S, Jasola S, KumarR (2012) A boundary restricted adaptive particle swarm optimization for data clustering. Int J Mach Learn Cybern. doi: 10.1007/s13042-012-0103-y

  16. Wang X, He Y, Dong L, Zhao H (2011) Particle swarm optimization for determining fuzzy measures from data. Inf Sci 181(19):4230–4252

    Article  MATH  Google Scholar 

Download references

Acknowledgments

This work is partially supported by Bursary of the University of Greenwich, “The Fundamental Research Funds for the Central Universities” (Project Number: JUSRP21012), the innovative research team project of Jiangnan University (Project Number: JNIRT0702), and National Natural Science Foundation of China (Project Number: 60973094).

Author information

Authors and Affiliations

  1. Department of Educational Technology, Jiangnan University, Wuxi, 214122, China

    Na Tian

  2. School of Computing and Mathematical Science, University of Greenwich, London, SE10 9LS, UK

    Choi-Hong Lai

Authors
  1. Na Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Choi-Hong Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Na Tian.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tian, N., Lai, CH. Parallel quantum-behaved particle swarm optimization. Int. J. Mach. Learn. & Cyber. 5, 309–318 (2014). https://doi.org/10.1007/s13042-013-0168-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-013-0168-2

Keywords

Search

Navigation