IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v9y2018i1d10.1007_s13198-016-0544-x.html
   My bibliography  Save this article

Recognition of noise source in multi sounds field by modified random localized based DE algorithm

Author

Listed:
  • Pravesh Kumar

    (Jaypee Institute of Information Technology)

  • Millie Pant

    (IIT Roorkee)

Abstract

Differential evolution (DE) algorithm is come out as a leading tool for solving many real life optimization problems since last few years. Modified random localized DE (MRLDE) is an enhance variant of DE algorithm use strategically way for selecting vectors to generate mutation vector. In this paper MRLDE is applied to a real life application of recognizing the location of noisy sources in multi noise plants which is an essential and prerequisite for noise control work. A trail noise method is utilized to find the variation between exact sound pressure level SPL and trial SPL at monitoring points and then MRLDE is implemented in combination with the technique of minimizing variation square in searching for the best locations and sound power level (SWLs). The experimental results expose that the significant SWLs and locations of noisy sources can be accurately detected by MRLDE.

Suggested Citation

  • Pravesh Kumar & Millie Pant, 2018. "Recognition of noise source in multi sounds field by modified random localized based DE algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(1), pages 245-261, February.
    • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:1:d:10.1007_s13198-016-0544-x
    DOI: 10.1007/s13198-016-0544-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-016-0544-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-016-0544-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kaelo, P. & Ali, M.M., 2006. "A numerical study of some modified differential evolution algorithms," European Journal of Operational Research, Elsevier, vol. 169(3), pages 1176-1184, March.
    2. Ali, M.M., 2007. "Differential evolution with preferential crossover," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1137-1147, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. M. Ali & W. Zhu, 2013. "A penalty function-based differential evolution algorithm for constrained global optimization," Computational Optimization and Applications, Springer, vol. 54(3), pages 707-739, April.
    2. Coelho, Leandro dos Santos, 2009. "Reliability–redundancy optimization by means of a chaotic differential evolution approach," Chaos, Solitons & Fractals, Elsevier, vol. 41(2), pages 594-602.
    3. Zio, E. & Viadana, G., 2011. "Optimization of the inspection intervals of a safety system in a nuclear power plant by Multi-Objective Differential Evolution (MODE)," Reliability Engineering and System Safety, Elsevier, vol. 96(11), pages 1552-1563.
    4. Piotrowski, Adam P. & Napiorkowski, Jaroslaw J. & Kiczko, Adam, 2012. "Differential Evolution algorithm with Separated Groups for multi-dimensional optimization problems," European Journal of Operational Research, Elsevier, vol. 216(1), pages 33-46.
    5. du Plessis, Mathys C. & Engelbrecht, Andries P., 2012. "Using Competitive Population Evaluation in a differential evolution algorithm for dynamic environments," European Journal of Operational Research, Elsevier, vol. 218(1), pages 7-20.
    6. Biswas (Raha), Syamasree & Mandal, Kamal Krishna & Chakraborty, Niladri, 2016. "Pareto-efficient double auction power transactions for economic reactive power dispatch," Applied Energy, Elsevier, vol. 168(C), pages 610-627.
    7. Kaelo, P. & Ali, M.M., 2007. "Integrated crossover rules in real coded genetic algorithms," European Journal of Operational Research, Elsevier, vol. 176(1), pages 60-76, January.
    8. Maysam Safe & Seyed Khazraee & Payam Setoodeh & Abdolhosein Jahanmiri, 2013. "Model reduction and optimization of a reactive dividing wall batch distillation column inspired by response surface methodology and differential evolution," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 19(1), pages 29-50.
    9. Rashida Adeeb Khanum & Muhammad Asif Jan & Nasser Mansoor Tairan & Wali Khan Mashwani, 2016. "Hybridization of Adaptive Differential Evolution with an Expensive Local Search Method," Journal of Optimization, Hindawi, vol. 2016, pages 1-14, July.
    10. Raghav Prasad Parouha & Pooja Verma, 2022. "An innovative hybrid algorithm for bound-unconstrained optimization problems and applications," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1273-1336, June.
    11. Fahad R. Albogamy, 2022. "Optimal Energy Consumption Scheduler Considering Real-Time Pricing Scheme for Energy Optimization in Smart Microgrid," Energies, MDPI, vol. 15(21), pages 1-31, October.
    12. Mohsen Davoodi & Hamed Jafari Kaleybar & Morris Brenna & Dario Zaninelli, 2023. "Energy Management Systems for Smart Electric Railway Networks: A Methodological Review," Sustainability, MDPI, vol. 15(16), pages 1-35, August.
    13. Ali, Musrrat. & Siarry, Patrick & Pant, Millie., 2012. "An efficient Differential Evolution based algorithm for solving multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 217(2), pages 404-416.
    14. Andreas C. Nearchou, 2018. "Multicriteria scheduling optimization using an elitist multiobjective population heuristic: the h-NSDE algorithm," Journal of Heuristics, Springer, vol. 24(6), pages 817-851, December.
    15. Ali, M.M., 2007. "Differential evolution with preferential crossover," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1137-1147, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ijsaem:v:9:y:2018:i:1:d:10.1007_s13198-016-0544-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.