IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/v47y2024i5d10.1007_s10878-024-01189-9.html
   My bibliography  Save this article

A hybrid grey wolf optimizer for engineering design problems

Author

Listed:
  • Shuilin Chen

    (Donghua University)

  • Jianguo Zheng

    (Donghua University)

Abstract

Grey wolf optimizer (GWO) is one of the most popular metaheuristics, and it has been presented as highly competitive with other comparison methods. However, the basic GWO needs some improvement, such as premature convergence and imbalance between exploitation and exploration. To address these weaknesses, this paper develops a hybrid grey wolf optimizer (HGWO), which combines the Halton sequence, dimension learning-based, crisscross strategy, and Cauchy mutation strategy. Firstly, the Halton sequence is used to enlarge the search scope and improve the diversity of the solutions. Then, the dimension learning-based is used for position update to balance exploitation and exploration. Furthermore, the crisscross strategy is introduced to enhance convergence precision. Finally, the Cauchy mutation strategy is adapted to avoid falling into the local optimum. The effectiveness of HGWO is demonstrated by comparing it with advanced algorithms on the 15 benchmark functions in different dimensions. The results illustrate that HGWO outperforms other advanced algorithms. Moreover, HGWO is used to solve eight real-world engineering problems, and the results demonstrate that HGWO is superior to different advanced algorithms.

Suggested Citation

  • Shuilin Chen & Jianguo Zheng, 2024. "A hybrid grey wolf optimizer for engineering design problems," Journal of Combinatorial Optimization, Springer, vol. 47(5), pages 1-53, July.
    • Handle: RePEc:spr:jcomop:v:47:y:2024:i:5:d:10.1007_s10878-024-01189-9
    DOI: 10.1007/s10878-024-01189-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-024-01189-9
    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/s10878-024-01189-9?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. Hashim, Fatma A. & Houssein, Essam H. & Hussain, Kashif & Mabrouk, Mai S. & Al-Atabany, Walid, 2022. "Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 84-110.
    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. Essam H. Houssein & Awny Sayed, 2023. "Dynamic Candidate Solution Boosted Beluga Whale Optimization Algorithm for Biomedical Classification," Mathematics, MDPI, vol. 11(3), pages 1-27, January.
    2. Zhang, Ziyuan & Wang, Jianzhou & Wei, Danxiang & Luo, Tianrui & Xia, Yurui, 2023. "A novel ensemble system for short-term wind speed forecasting based on Two-stage Attention-Based Recurrent Neural Network," Renewable Energy, Elsevier, vol. 204(C), pages 11-23.
    3. Jian Zhao & Bochen Zhang & Xiwang Guo & Liang Qi & Zhiwu Li, 2022. "Self-Adapting Spherical Search Algorithm with Differential Evolution for Global Optimization," Mathematics, MDPI, vol. 10(23), pages 1-31, November.
    4. Hegazy Rezk & A. G. Olabi & Mohammad Ali Abdelkareem & Abdul Hai Alami & Enas Taha Sayed, 2023. "Optimal Parameter Determination of Membrane Bioreactor to Boost Biohydrogen Production-Based Integration of ANFIS Modeling and Honey Badger Algorithm," Sustainability, MDPI, vol. 15(2), pages 1-13, January.
    5. Fatmah Y. Assiri & Mahmoud Ragab, 2023. "Optimal Deep-Learning-Based Cyberattack Detection in a Blockchain-Assisted IoT Environment," Mathematics, MDPI, vol. 11(19), pages 1-16, September.
    6. Eslami, N. & Yazdani, S. & Mirzaei, M. & Hadavandi, E., 2022. "Aphid–Ant Mutualism: A novel nature-inspired​ metaheuristic algorithm for solving optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 362-395.
    7. Mahamed G. H. Omran & Maurice Clerc & Fatme Ghaddar & Ahmad Aldabagh & Omar Tawfik, 2022. "Permutation Tests for Metaheuristic Algorithms," Mathematics, MDPI, vol. 10(13), pages 1-15, June.
    8. Muhyaddin Rawa & Abdullah Abusorrah & Yusuf Al-Turki & Martin Calasan & Mihailo Micev & Ziad M. Ali & Saad Mekhilef & Hussain Bassi & Hatem Sindi & Shady H. E. Abdel Aleem, 2022. "Estimation of Parameters of Different Equivalent Circuit Models of Solar Cells and Various Photovoltaic Modules Using Hybrid Variants of Honey Badger Algorithm and Artificial Gorilla Troops Optimizer," Mathematics, MDPI, vol. 10(7), pages 1-31, March.
    9. Muhammad Haris Khan & Abasin Ulasyar & Abraiz Khattak & Haris Sheh Zad & Mohammad Alsharef & Ahmad Aziz Alahmadi & Nasim Ullah, 2022. "Optimal Sizing and Allocation of Distributed Generation in the Radial Power Distribution System Using Honey Badger Algorithm," Energies, MDPI, vol. 15(16), pages 1-18, August.
    10. Chenyang Gao & Teng Li & Yuelin Gao & Ziyu Zhang, 2024. "A Comprehensive Multi-Strategy Enhanced Biogeography-Based Optimization Algorithm for High-Dimensional Optimization and Engineering Design Problems," Mathematics, MDPI, vol. 12(3), pages 1-35, January.
    11. Chao Zhou & Bing Gao & Haiyue Yang & Xudong Zhang & Jiaqi Liu & Lingling Li, 2022. "Junction Temperature Prediction of Insulated-Gate Bipolar Transistors in Wind Power Systems Based on an Improved Honey Badger Algorithm," Energies, MDPI, vol. 15(19), pages 1-19, October.
    12. Murtadha Al-Kaabi & Virgil Dumbrava & Mircea Eremia, 2022. "A Slime Mould Algorithm Programming for Solving Single and Multi-Objective Optimal Power Flow Problems with Pareto Front Approach: A Case Study of the Iraqi Super Grid High Voltage," Energies, MDPI, vol. 15(20), pages 1-33, October.
    13. Yang, Xiaohui & Zhang, Zhonglian & Mei, Linghao & Wang, Xiaopeng & Deng, Yeheng & Wei, Shi & Liu, Xiaoping, 2023. "Optimal configuration of improved integrated energy system based on stepped carbon penalty response and improved power to gas," Energy, Elsevier, vol. 263(PD).
    14. Zhang, Zhonglian & Yang, Xiaohui & Yang, Li & Wang, Zhaojun & Huang, Zezhong & Wang, Xiaopeng & Mei, Linghao, 2023. "Optimal configuration of double carbon energy system considering climate change," Energy, Elsevier, vol. 283(C).
    15. Ghareeb Moustafa & Mostafa Elshahed & Ahmed R. Ginidi & Abdullah M. Shaheen & Hany S. E. Mansour, 2023. "A Gradient-Based Optimizer with a Crossover Operator for Distribution Static VAR Compensator (D-SVC) Sizing and Placement in Electrical Systems," Mathematics, MDPI, vol. 11(5), pages 1-30, February.
    16. Ren, Xin-Yu & Li, Ling-Ling & Ji, Bing-Xiang & Liu, Jia-Qi, 2024. "Design and analysis of solar hybrid combined cooling, heating and power system: A bi-level optimization model," Energy, Elsevier, vol. 292(C).
    17. Arup Das & Subhojit Dawn & Sadhan Gope & Taha Selim Ustun, 2022. "A Strategy for System Risk Mitigation Using FACTS Devices in a Wind Incorporated Competitive Power System," Sustainability, MDPI, vol. 14(13), pages 1-21, July.
    18. Hengfei Yang & Shiyuan Yang & Debiao Meng & Chenghao Hu & Chaosheng Wu & Bo Yang & Peng Nie & Yuan Si & Xiaoyan Su, 2024. "Optimization of Analog Circuit Parameters Using Bidirectional Long Short-Term Memory Coupled with an Enhanced Whale Optimization Algorithm," Mathematics, MDPI, vol. 13(1), pages 1-24, December.
    19. Kaveh, Mehrdad & Mesgari, Mohammad Saadi & Saeidian, Bahram, 2023. "Orchard Algorithm (OA): A new meta-heuristic algorithm for solving discrete and continuous optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 208(C), pages 95-135.
    20. Pan, Jeng-Shyang & Zhang, Li-Gang & Wang, Ruo-Bin & Snášel, Václav & Chu, Shu-Chuan, 2022. "Gannet optimization algorithm : A new metaheuristic algorithm for solving engineering optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 202(C), pages 343-373.

    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:jcomop:v:47:y:2024:i:5:d:10.1007_s10878-024-01189-9. 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.