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

Author

Listed:
  • Y. L. Zheng

    (Hubei University)

  • S. L. Nie

    (Beijing University of Technology)

  • H. Ji

    (Beijing University of Technology)

  • Z. Hu

    (Missouri University of Science and Technology)

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.

Suggested Citation

  • Y. L. Zheng & S. L. Nie & H. Ji & Z. Hu, 2013. "Application of a Fuzzy Programming Through Stochastic Particle Swarm Optimization to Assessment of Filter Management Strategies in Fluid Power System Under Uncertainty," Journal of Optimization Theory and Applications, Springer, vol. 157(1), pages 276-286, April.
    • Handle: RePEc:spr:joptap:v:157:y:2013:i:1:d:10.1007_s10957-012-0152-0
    DOI: 10.1007/s10957-012-0152-0
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    References listed on IDEAS

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    1. Matarazzo, B. & Munda, G., 1996. "New Approaches for the Comparison of L-R Fuzzy Numbers : a Theoretical and Operational Analysis," UFAE and IAE Working Papers 346.96, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
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    Cited by:

    1. Albanese Claudio & Armenti Yannick & Crépey Stéphane, 2020. "XVA metrics for CCP optimization," Statistics & Risk Modeling, De Gruyter, vol. 37(1-2), pages 25-53, January.
    2. Sabelfeld Karl K. & Levykin Alexander I. & Kireeva Anastasiya E., 2015. "Stochastic simulation of fluctuation-induced reaction-diffusion kinetics governed by Smoluchowski equations," Monte Carlo Methods and Applications, De Gruyter, vol. 21(1), pages 33-48, March.
    3. Vladimir Stojanovic & Novak Nedic, 2016. "A Nature Inspired Parameter Tuning Approach to Cascade Control for Hydraulically Driven Parallel Robot Platform," Journal of Optimization Theory and Applications, Springer, vol. 168(1), pages 332-347, January.

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