IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v11y2020i2d10.1007_s13198-019-00860-3.html
   My bibliography  Save this article

Air pollution emissions control using shuffled frog leaping algorithm

Author

Listed:
  • Tarun Kumar Sharma

    (Shobhit University Gangoh)

  • Divya Prakash

    (Shobhit University Gangoh)

Abstract

Shuffled frog leaping (SFL) algorithm is a recently introduced metaheuristic which mimics the foraging process of frogs. SFL performs exploration as well as exploitation. In SFL algorithm the colony of frogs is divided into several memeplexes. In each memeplexes frog perform independent social cooperative local search and in later stages this information is shared among memeplexes. The process of sharing the information is shuffling process. SFL has been successfully applied to solve various real world optimization problems. In the present study SFL algorithm is implemented on a very interesting and challenging issue of optimization of Air pollution emissions using different control technologies. The nature of the problem is mixed integer linear programming problem. To further validate the efficacy of the algorithm shipping problem is also solved. The simulated results demonstrate the effectiveness of SFL algorithm.

Suggested Citation

  • Tarun Kumar Sharma & Divya Prakash, 2020. "Air pollution emissions control using shuffled frog leaping 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. 11(2), pages 332-339, April.
    • Handle: RePEc:spr:ijsaem:v:11:y:2020:i:2:d:10.1007_s13198-019-00860-3
    DOI: 10.1007/s13198-019-00860-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-019-00860-3
    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/s13198-019-00860-3?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. Dash, Rajashree, 2017. "An improved shuffled frog leaping algorithm based evolutionary framework for currency exchange rate prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 782-796.
    2. Tarun Kumar Sharma & Millie Pant, 2017. "Distribution in the placement of food in artificial bee colony based on changing factor," 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. 8(1), pages 159-172, March.
    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. Fu, Sibao & Li, Yongwu & Sun, Shaolong & Li, Hongtao, 2019. "Evolutionary support vector machine for RMB exchange rate forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 692-704.
    2. Samuka Mohanty & Rajashree Dash, 2022. "Neural Network-Based Bitcoin Pricing Using a New Mutated Climb Monkey Algorithm with TOPSIS Analysis for Sustainable Development," Mathematics, MDPI, vol. 10(22), pages 1-23, November.
    3. Tripathi Manas & Kumar Saurabh & Inani Sarveshwar Kumar, 2021. "Exchange Rate Forecasting Using Ensemble Modeling for Better Policy Implications," Journal of Time Series Econometrics, De Gruyter, vol. 13(1), pages 43-71, January.

    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:11:y:2020:i:2:d:10.1007_s13198-019-00860-3. 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.