IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i4p851-d1060776.html
   My bibliography  Save this article

EJS: Multi-Strategy Enhanced Jellyfish Search Algorithm for Engineering Applications

Author

Listed:
  • Gang Hu

    (Department of Applied Mathematics, Xi’an University of Technology, Xi’an 710054, China)

  • Jiao Wang

    (School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Min Li

    (Department of Applied Mathematics, Xi’an University of Technology, Xi’an 710054, China)

  • Abdelazim G. Hussien

    (Department of Computer and Information Science, Linköping University, 581 83 Linköping, Sweden
    Faculty of Science, Fayoum University, Faiyum 63514, Egypt)

  • Muhammad Abbas

    (Department of Mathematics, University of Sargodha, Sargodha 40100, Pakistan)

Abstract

The jellyfish search (JS) algorithm impersonates the foraging behavior of jellyfish in the ocean. It is a newly developed metaheuristic algorithm that solves complex and real-world optimization problems. The global exploration capability and robustness of the JS algorithm are strong, but the JS algorithm still has significant development space for solving complex optimization problems with high dimensions and multiple local optima. Therefore, in this study, an enhanced jellyfish search (EJS) algorithm is developed, and three improvements are made: (i) By adding a sine and cosine learning factors strategy, the jellyfish can learn from both random individuals and the best individual during Type B motion in the swarm to enhance optimization capability and accelerate convergence speed. (ii) By adding a local escape operator, the algorithm can skip the trap of local optimization, and thereby, can enhance the exploitation ability of the JS algorithm. (iii) By applying an opposition-based learning and quasi-opposition learning strategy, the population distribution is increased, strengthened, and more diversified, and better individuals are selected from the present and the new opposition solution to participate in the next iteration, which can enhance the solution’s quality, meanwhile, convergence speed is faster and the algorithm’s precision is increased. In addition, the performance of the developed EJS algorithm was compared with those of the incomplete improved algorithms, and some previously outstanding and advanced methods were evaluated on the CEC2019 test set as well as six examples of real engineering cases. The results demonstrate that the EJS algorithm can skip the trap of local optimization, can enhance the solution’s quality, and can increase the calculation speed. In addition, the practical engineering applications of the EJS algorithm also verify its superiority and effectiveness in solving both constrained and unconstrained optimization problems, and therefore, suggests future possible applications for solving such optimization problems.

Suggested Citation

  • Gang Hu & Jiao Wang & Min Li & Abdelazim G. Hussien & Muhammad Abbas, 2023. "EJS: Multi-Strategy Enhanced Jellyfish Search Algorithm for Engineering Applications," Mathematics, MDPI, vol. 11(4), pages 1-32, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:851-:d:1060776
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/4/851/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/4/851/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohamed Abdel-Basset & Reda Mohamed & Ripon K. Chakrabortty & Michael J. Ryan & Attia El-Fergany, 2021. "An Improved Artificial Jellyfish Search Optimizer for Parameter Identification of Photovoltaic Models," Energies, MDPI, vol. 14(7), pages 1-33, March.
    2. Hu, Gang & Dou, Wanting & Wang, Xiaofeng & Abbas, Muhammad, 2022. "An enhanced chimp optimization algorithm for optimal degree reduction of Said–Ball curves," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 197(C), pages 207-252.
    3. Chou, Jui-Sheng & Truong, Dinh-Nhat, 2021. "A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean," Applied Mathematics and Computation, Elsevier, vol. 389(C).
    4. Rana Muhammad Adnan & Hong-Liang Dai & Reham R. Mostafa & Kulwinder Singh Parmar & Salim Heddam & Ozgur Kisi, 2022. "Modeling Multistep Ahead Dissolved Oxygen Concentration Using Improved Support Vector Machines by a Hybrid Metaheuristic Algorithm," Sustainability, MDPI, vol. 14(6), pages 1-23, March.
    5. Nesma M Ashraf & Reham R Mostafa & Rasha H Sakr & M Z Rashad, 2021. "Optimizing hyperparameters of deep reinforcement learning for autonomous driving based on whale optimization algorithm," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-24, June.
    6. Gouda, Eid A. & Kotb, Mohamed F. & El-Fergany, Attia A., 2021. "Jellyfish search algorithm for extracting unknown parameters of PEM fuel cell models: Steady-state performance and analysis," Energy, Elsevier, vol. 221(C).
    7. Shaheen, Abdullah M. & El-Sehiemy, Ragab A. & Alharthi, Mosleh M. & Ghoneim, Sherif S.M. & Ginidi, Ahmed R., 2021. "Multi-objective jellyfish search optimizer for efficient power system operation based on multi-dimensional OPF framework," Energy, Elsevier, vol. 237(C).
    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. Ahmed Ginidi & Sherif M. Ghoneim & Abdallah Elsayed & Ragab El-Sehiemy & Abdullah Shaheen & Attia El-Fergany, 2021. "Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems," Sustainability, MDPI, vol. 13(16), pages 1-28, August.
    2. Mohamed Abdel-Basset & Reda Mohamed & Attia El-Fergany & Sameh S. Askar & Mohamed Abouhawwash, 2021. "Efficient Ranking-Based Whale Optimizer for Parameter Extraction of Three-Diode Photovoltaic Model: Analysis and Validations," Energies, MDPI, vol. 14(13), pages 1-20, June.
    3. Liqiong Huang & Yuanyuan Wang & Yuxuan Guo & Gang Hu, 2022. "An Improved Reptile Search Algorithm Based on Lévy Flight and Interactive Crossover Strategy to Engineering Application," Mathematics, MDPI, vol. 10(13), pages 1-39, July.
    4. Abdullah Shaheen & Ragab El-Sehiemy & Salah Kamel & Ali Selim, 2022. "Optimal Operational Reliability and Reconfiguration of Electrical Distribution Network Based on Jellyfish Search Algorithm," Energies, MDPI, vol. 15(19), pages 1-14, September.
    5. Ibrahim Attiya & Laith Abualigah & Samah Alshathri & Doaa Elsadek & Mohamed Abd Elaziz, 2022. "Dynamic Jellyfish Search Algorithm Based on Simulated Annealing and Disruption Operators for Global Optimization with Applications to Cloud Task Scheduling," Mathematics, MDPI, vol. 10(11), pages 1-23, June.
    6. Jianwei Yang & Zhen Liu & Xin Zhang & Gang Hu, 2022. "Elite Chaotic Manta Ray Algorithm Integrated with Chaotic Initialization and Opposition-Based Learning," Mathematics, MDPI, vol. 10(16), pages 1-34, August.
    7. Husham Muayad Nayyef & Ahmad Asrul Ibrahim & Muhammad Ammirrul Atiqi Mohd Zainuri & Mohd Asyraf Zulkifley & Hussain Shareef, 2023. "A Novel Hybrid Algorithm Based on Jellyfish Search and Particle Swarm Optimization," Mathematics, MDPI, vol. 11(14), pages 1-29, July.
    8. Rafa Elshara & Aybaba Hançerlioğullari & Javad Rahebi & Jose Manuel Lopez-Guede, 2024. "PV Cells and Modules Parameter Estimation Using Coati Optimization Algorithm," Energies, MDPI, vol. 17(7), pages 1-26, April.
    9. Ahmed Ginidi & Abdallah Elsayed & Abdullah Shaheen & Ehab Elattar & Ragab El-Sehiemy, 2021. "An Innovative Hybrid Heap-Based and Jellyfish Search Algorithm for Combined Heat and Power Economic Dispatch in Electrical Grids," Mathematics, MDPI, vol. 9(17), pages 1-25, August.
    10. Ghareeb Moustafa & Ali M. El-Rifaie & Idris H. Smaili & Ahmed Ginidi & Abdullah M. Shaheen & Ahmed F. Youssef & Mohamed A. Tolba, 2023. "An Enhanced Dwarf Mongoose Optimization Algorithm for Solving Engineering Problems," Mathematics, MDPI, vol. 11(15), pages 1-26, July.
    11. Mohamed Abdel-Basset & Reda Mohamed & Ripon K. Chakrabortty & Michael J. Ryan & Attia El-Fergany, 2021. "An Improved Artificial Jellyfish Search Optimizer for Parameter Identification of Photovoltaic Models," Energies, MDPI, vol. 14(7), pages 1-33, March.
    12. Guarino, Antonio & Trinchero, Riccardo & Canavero, Flavio & Spagnuolo, Giovanni, 2022. "A fast fuel cell parametric identification approach based on machine learning inverse models," Energy, Elsevier, vol. 239(PC).
    13. Shahenda Sarhan & Abdullah Shaheen & Ragab El-Sehiemy & Mona Gafar, 2023. "An Augmented Social Network Search Algorithm for Optimal Reactive Power Dispatch Problem," Mathematics, MDPI, vol. 11(5), pages 1-42, March.
    14. 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.
    15. Hu, Gang & Yang, Rui & Wei, Guo, 2023. "Hybrid chameleon swarm algorithm with multi-strategy: A case study of degree reduction for disk Wang–Ball curves," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 206(C), pages 709-769.
    16. Thirunavukkarasu, M. & Sawle, Yashwant & Lala, Himadri, 2023. "A comprehensive review on optimization of hybrid renewable energy systems using various optimization techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 176(C).
    17. Hesham Alhumade & Iqbal Ahmed Moujdin & Saad Al-Shahrani, 2023. "Increasing Output Power of a Microfluidic Fuel Cell Using Fuzzy Modeling and Jellyfish Search Optimization," Sustainability, MDPI, vol. 15(14), pages 1-15, July.
    18. Andrew J. Riad & Hany M. Hasanien & Rania A. Turky & Ahmed H. Yakout, 2023. "Identifying the PEM Fuel Cell Parameters Using Artificial Rabbits Optimization Algorithm," Sustainability, MDPI, vol. 15(5), pages 1-17, March.
    19. Shaheen, Abdullah M. & El-Sehiemy, Ragab A. & Elattar, Ehab & Ginidi, Ahmed R., 2022. "An Amalgamated Heap and Jellyfish Optimizer for economic dispatch in Combined heat and power systems including N-1 Unit outages," Energy, Elsevier, vol. 246(C).
    20. Araby Mahdy & Abdullah Shaheen & Ragab El-Sehiemy & Ahmed Ginidi & Saad F. Al-Gahtani, 2023. "Single- and Multi-Objective Optimization Frameworks of Shape Design of Tubular Linear Synchronous Motor," Energies, MDPI, vol. 16(5), pages 1-27, March.

    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:gam:jmathe:v:11:y:2023:i:4:p:851-:d:1060776. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.