Skip to main content
Springer Nature Link
Log in

Application of a Fuzzy Programming Through Stochastic Particle Swarm Optimization to Assessment of Filter Management Strategies in Fluid Power System Under Uncertainty

  • Published:
Journal of Optimization Theory and Applications Aims and scope Submit manuscript

Abstract

A fuzzy programming through stochastic particle swarm optimization is developed for the assessment of filter allocation and replacement strategies in fluid power system (FPS) under uncertainty. It can not only handle uncertainties expressed as L-R fuzzy numbers but also enhance the system robustness by transforming the fuzzy inequalities into inclusive constraints. As the simulation results indicate, the developed model can successfully decrease the total cost and enhanced the safety of system. Generally, it is believed that the model can help identify excellent filter allocation and replacement strategy with minimized operation cost and system failure risk while protecting the system.

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

Similar content being viewed by others

References

  1. Tu, Q.Z., Gong, L.H., Wang, Q.: Modeling for the hydraulic contamination control system. China Mech. Eng. 12(6), 629–631 (2001)

    Google Scholar 

  2. Poeth, B.: Proactive maintenance. Fluid Power J. 12(1), 28–30 (2005)

    Google Scholar 

  3. Nirmal, K.M., Tapan, K.R.: A displayed inventory model with L-R fuzzy number. Fuzzy Optim. Decis. Mak. 5, 227–243 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  4. Matarazzo, B., Munda, G.: New approaches for the comparison of L-R fuzzy numbers: a theoretical and operational analysis. Fuzzy Sets Syst. 118(3), 407–418 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  5. Nie, S.L., Zheng, Y.L., Li, Y.P., Peng, S., Huang, G.H.: IFCIP: an integrated optimization method for planning filters in fluid power systems under uncertainty. Eng. Optim. 43(3), 329–348 (2011)

    Article  Google Scholar 

  6. Nie, S.L., Xiong, Z.B., Li, Y.P., Huang, G.H.: An improved fuzzy programming model with an L-R fuzzy number for filter management strategies in fluid power systems under uncertainty. Proc. Inst. Mech. Eng., Part C, J. Mech. Eng. Sci. 224(9), 2011–2026 (2010)

    Article  Google Scholar 

  7. Nie, S.L., Wen, J.D., Li, Y.P., Tang, X.H., Huang, G.H.: Filter allocation and replacement strategies in fluid power system under uncertainty: a fuzzy robust nonlinear programming approach. Optim. Eng. 13(2), 319–347 (2012)

    Article  MathSciNet  Google Scholar 

  8. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference Neural Networks, vol. 4, pp. 1942–1948 (1995)

    Chapter  Google Scholar 

  9. Zeng, J.C., Cui, Z.H.: A guaranteed global particle swarm optimizer. J. Comput. Res. Dev. 41, 1333–1338 (2004) (in Chinese)

    Google Scholar 

  10. Kennedy, J.: The particle swarm: social adaptation of knowledge. In: Proceedings of IEEE International Conference on Evolutional Computation, Indianapolis, Indiana, pp. 303–308. IEEE Service Center, Piscataway (1997)

    Google Scholar 

  11. Coello, C.A.: Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput. Methods Appl. Mech. Eng. 191(11–12), 1245–1287 (2002)

    Article  MATH  Google Scholar 

  12. Zielinski, K., Weitkemper, P., Laur, R., Kammeyer, K.D.: Parameter study for differential evolution using a power allocation problem including interference cancellation. In: Proceedings of IEEE Congress Evolutionary Computation, Vancouver, BC, Canada, pp. 6748–6755 (2006)

    Google Scholar 

  13. Zielinski, K., Weitkemper, P., Laur, R., Kammeyer, K.D.: Optimization of power allocation for interference cancellation with particle swarm optimization. IEEE Trans. Evol. Comput. 13(1), 128–150 (2009)

    Article  Google Scholar 

Download references

Acknowledgements

This research was funded by Natural Science Foundations of China (No. 51075007), and National High-tech R&D (863) Program (No. 2012AA091103).

Author information

Authors and Affiliations

  1. School of Mathematics and Computer Science, Hubei University, Wuhan, 430062, China

    Y. L. Zheng

  2. College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, 100124, China

    S. L. Nie & H. Ji

  3. Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, MO, 65401, USA

    Z. Hu

Authors
  1. Y. L. Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. L. Nie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H. Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Z. Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. L. Nie.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zheng, Y.L., Nie, S.L., Ji, H. et al. Application of a Fuzzy Programming Through Stochastic Particle Swarm Optimization to Assessment of Filter Management Strategies in Fluid Power System Under Uncertainty. J Optim Theory Appl 157, 276–286 (2013). https://doi.org/10.1007/s10957-012-0152-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10957-012-0152-0

Keywords