IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v15y2024i12d10.1007_s13198-024-02556-9.html
   My bibliography  Save this article

Dynamic motion based evolutionary algorithm for enhancement of the search capability for global search space

Author

Listed:
  • Nidhi Parashar

    (Meerut Institute of Technology)

  • Deependra Rastogi

    (IILM University)

  • Prashant Johri

    (Galgotias University)

  • Sunil Kumar Khatri

    (Amity University)

  • Sudeept Singh Yadav

    (Galgotias University)

  • Methily Johri

    (Galgotias University)

Abstract

The stagnation problem in evolutionary algorithms reduces the optimization algorithm’s performance after a certain number of iterations, leading to diversity loss. In this article, five new mutation operators using Random, Random-Best, Best, Current Random, and Current-Best-Random selection policies based on dynamic motion strategy according to feasible environments from global search space are added to the Differential Evolution DE) process. The proposed strategy supports the diversity of the problem’s nature while maintaining the global search and preserving environmental balance. The validation process has involved two tests. First, the testing and comparative study results showed that the proposed method performed satisfactorily at the target value of $$10^{-8}$$ 10 - 8 for the bare minimum of statistical and function evaluations. In the second validation test, the proposed operators pass the Wilcoxon rank-sum test for the lowest error in each function (p = 0.05). The proposed approaches show an improvement in the search capability of evolutionary optimization algorithms.

Suggested Citation

  • Nidhi Parashar & Deependra Rastogi & Prashant Johri & Sunil Kumar Khatri & Sudeept Singh Yadav & Methily Johri, 2024. "Dynamic motion based evolutionary algorithm for enhancement of the search capability for global search space," 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. 15(12), pages 5653-5675, December.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:12:d:10.1007_s13198-024-02556-9
    DOI: 10.1007/s13198-024-02556-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-024-02556-9
    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/s13198-024-02556-9?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. Ali Wagdy Mohamed, 2018. "A novel differential evolution algorithm for solving constrained engineering optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 659-692, March.
    2. Ali Wagdy Mohamed & Ali Khater Mohamed & Ehab Z. Elfeky & Mohamed Saleh, 2019. "Enhanced Directed Differential Evolution Algorithm for Solving Constrained Engineering Optimization Problems," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 10(1), pages 1-28, January.
    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. Chao Huang & Zhenyu Zhao & Qingwen Li & Xiong Luo & Long Wang, 2024. "Wind Power Bidding Based on an Ensemble Differential Evolution Algorithm with a Problem-Specific Constraint-Handling Technique," Energies, MDPI, vol. 17(2), pages 1-14, January.
    2. Wenchao Yi & Liang Gao & Zhi Pei & Jiansha Lu & Yong Chen, 2021. "ε Constrained differential evolution using halfspace partition for optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 157-178, January.
    3. He, Jiao & Jin, Xin & Xie, S.Y. & Cao, Le & Lin, Yifan & Wang, Ning, 2019. "Multi-body dynamics modeling and TMD optimization based on the improved AFSA for floating wind turbines," Renewable Energy, Elsevier, vol. 141(C), pages 305-321.
    4. Khalid Abdulaziz Alnowibet & Salem Mahdi & Mahmoud El-Alem & Mohamed Abdelawwad & Ali Wagdy Mohamed, 2022. "Guided Hybrid Modified Simulated Annealing Algorithm for Solving Constrained Global Optimization Problems," Mathematics, MDPI, vol. 10(8), pages 1-25, April.
    5. Aggarwal, Sakshi & Mishra, Krishn K., 2023. "X-MODE: Extended Multi-operator Differential Evolution algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 211(C), pages 85-108.
    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. Umesh Balande & Deepti Shrimankar, 2019. "SRIFA: Stochastic Ranking with Improved-Firefly-Algorithm for Constrained Optimization Engineering Design Problems," Mathematics, MDPI, vol. 7(3), pages 1-26, March.
    8. Yiying Zhang & Zhigang Jin, 2022. "Comprehensive learning Jaya algorithm for engineering design optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1229-1253, June.
    9. Zhang, Jinzhong & Zhang, Gang & Kong, Min & Zhang, Tan & Wang, Duansong & Chen, Rui, 2023. "CWOA: A novel complex-valued encoding whale optimization algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 151-188.

    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:15:y:2024:i:12:d:10.1007_s13198-024-02556-9. 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.