IDEAS home Printed from https://ideas.repec.org/a/spr/opsear/v61y2024i4d10.1007_s12597-024-00763-3.html
   My bibliography  Save this article

Modified differential evolution to solve systems of nonlinear equations

Author

Listed:
  • Pooja Tiwari

    (Indira Gandhi National Tribal University)

  • Vishnu Narayan Mishra

    (Indira Gandhi National Tribal University)

  • Raghav Prasad Parouha

    (Indira Gandhi National Tribal University)

Abstract

Solving systems of nonlinear equations (NESs) is a significant and challenging task in the field of sciences and engineering. This article proposes a modified differential evolution (MDE) to solve the NESs problem. Using principle of the particle swarm optimization, it employed novel mutation structure with modernistic control factors, to heighten exploration and exploitation activities. Correspondingly, a different crossover rate is presented in MDE, to enrich the convergence rapidity. Lastly, a new selection system is familiarized in MDE, to estimate diversity and evade from the local minimum. To certify the performance of MDE, thirteen typical benchmark test suites are solved. Additionally, optimize the seven cases of NESs, to prove the applicability of MDE. The experimental and comparative outcomes indicate that the suggested MDE performs more effectively on complex test functions and NESs.

Suggested Citation

  • Pooja Tiwari & Vishnu Narayan Mishra & Raghav Prasad Parouha, 2024. "Modified differential evolution to solve systems of nonlinear equations," OPSEARCH, Springer;Operational Research Society of India, vol. 61(4), pages 1968-2001, December.
    • Handle: RePEc:spr:opsear:v:61:y:2024:i:4:d:10.1007_s12597-024-00763-3
    DOI: 10.1007/s12597-024-00763-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12597-024-00763-3
    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/s12597-024-00763-3?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. Haihuang Huang & Liwei Jiang & Xue Yu & Dongqing Xie, 2018. "Hypercube-Based Crowding Differential Evolution with Neighborhood Mutation for Multimodal Optimization," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 9(2), pages 15-27, April.
    2. Ali, M.M., 2007. "Differential evolution with preferential crossover," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1137-1147, September.
    3. Gai-Ge Wang & Suash Deb & Xinchao Zhao & Zhihua Cui, 2018. "A new monarch butterfly optimization with an improved crossover operator," Operational Research, Springer, vol. 18(3), pages 731-755, October.
    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. Gui Li & Gai-Ge Wang & Shan Wang, 2021. "Two-Population Coevolutionary Algorithm with Dynamic Learning Strategy for Many-Objective Optimization," Mathematics, MDPI, vol. 9(4), pages 1-34, February.
    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. Cheng-Long Wei & Gai-Ge Wang, 2020. "Hybrid Annealing Krill Herd and Quantum-Behaved Particle Swarm Optimization," Mathematics, MDPI, vol. 8(9), pages 1-23, August.
    4. Jiang Li & Lihong Guo & Yan Li & Chang Liu, 2019. "Enhancing Elephant Herding Optimization with Novel Individual Updating Strategies for Large-Scale Optimization Problems," Mathematics, MDPI, vol. 7(5), pages 1-35, April.
    5. Nebojsa Bacanin & Timea Bezdan & Eva Tuba & Ivana Strumberger & Milan Tuba, 2020. "Monarch Butterfly Optimization Based Convolutional Neural Network Design," Mathematics, MDPI, vol. 8(6), pages 1-33, June.
    6. 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.
    7. Chen, Chengcheng & Wang, Xianchang & Yu, Helong & Wang, Mingjing & Chen, Huiling, 2021. "Dealing with multi-modality using synthesis of Moth-flame optimizer with sine cosine mechanisms," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 188(C), pages 291-318.
    8. Juan Li & Dan-dan Xiao & Hong Lei & Ting Zhang & Tian Tian, 2020. "Using Cuckoo Search Algorithm with Q -Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location," Mathematics, MDPI, vol. 8(2), pages 1-32, January.
    9. 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.
    10. 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.
    11. Minhee Kim & Junjae Chae, 2019. "Monarch Butterfly Optimization for Facility Layout Design Based on a Single Loop Material Handling Path," Mathematics, MDPI, vol. 7(2), pages 1-21, February.

    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:opsear:v:61:y:2024:i:4:d:10.1007_s12597-024-00763-3. 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.