IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/2571863.html
   My bibliography  Save this article

Dingo Optimizer: A Nature-Inspired Metaheuristic Approach for Engineering Problems

Author

Listed:
  • Amit Kumar Bairwa
  • Sandeep Joshi
  • Dilbag Singh

Abstract

Optimization is a buzzword, whenever researchers think of engineering problems. This paper presents a new metaheuristic named dingo optimizer (DOX) which is motivated by the behavior of dingo ( Canis familiaris dingo ). The overall concept is to develop this method involving the collaborative and social behavior of dingoes. The developed algorithm is based on the hunting behavior of dingoes that includes exploration, encircling, and exploitation. All the above prey hunting steps are modeled mathematically and are implemented in the simulator to test the performance of the proposed algorithm. Comparative analyses are drawn among the proposed approach and grey wolf optimizer (GWO) and particle swarm optimizer (PSO). Some of the well-known test functions are used for the comparative study of this work. The results reveal that the dingo optimizer performed significantly better than other nature-inspired algorithms.

Suggested Citation

  • Amit Kumar Bairwa & Sandeep Joshi & Dilbag Singh, 2021. "Dingo Optimizer: A Nature-Inspired Metaheuristic Approach for Engineering Problems," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-12, June.
    • Handle: RePEc:hin:jnlmpe:2571863
    DOI: 10.1155/2021/2571863
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2021/2571863.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2021/2571863.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2021/2571863?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shuangqing Chen & Shanlong Wang & Minghu Jiang & Yuchun Li & Lan Meng & Bing Guan & Ze Yu, 2024. "Layout Reconstruction Optimization Method of Oil-Gathering Systems for Oilfields in the Mid to Late Stage of Development Based on the Arithmetic–Fireworks Optimization Algorithm," Mathematics, MDPI, vol. 12(18), pages 1-39, September.
    2. Mohamed Abdel-Basset & Reda Mohamed & Victor Chang, 2021. "An Efficient Parameter Estimation Algorithm for Proton Exchange Membrane Fuel Cells," Energies, MDPI, vol. 14(21), pages 1-23, November.
    3. Samson Oladayo Ayanlade & Funso Kehinde Ariyo & Abdulrasaq Jimoh & Kayode Timothy Akindeji & Adeleye Oluwaseye Adetunji & Emmanuel Idowu Ogunwole & Dolapo Eniola Owolabi, 2023. "Optimal Allocation of Photovoltaic Distributed Generations in Radial Distribution Networks," Sustainability, MDPI, vol. 15(18), pages 1-26, September.
    4. Mohammed Alkahtani & Mustufa Haider Abidi & Hamoud S. Bin Obaid & Osama Alotaik, 2023. "Modified Gannet Optimization Algorithm for Reducing System Operation Cost in Engine Parts Industry with Pooling Management and Transport Optimization," Sustainability, MDPI, vol. 15(18), pages 1-21, September.
    5. Dahu Li & Hongyu Zhou & Yuan Chen & Yue Zhou & Yuze Rao & Wei Yao, 2023. "A Frequency Support Approach for Hybrid Energy Systems Considering Energy Storage," Energies, MDPI, vol. 16(10), pages 1-16, May.

    More about this item

    Statistics

    Access and download statistics

    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:hin:jnlmpe:2571863. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.