IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i24p8575-d706258.html
   My bibliography  Save this article

A Multimodal Improved Particle Swarm Optimization for High Dimensional Problems in Electromagnetic Devices

Author

Listed:
  • Rehan Ali Khan

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Shiyou Yang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Shafiullah Khan

    (Department of Electronics, Islamia College University, Peshawar 25000, Pakistan)

  • Shah Fahad

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Kalimullah

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

Particle Swarm Optimization (PSO) is a member of the swarm intelligence-based on a metaheuristic approach which is inspired by the natural deeds of bird flocking and fish schooling. In comparison to other traditional methods, the model of PSO is widely recognized as a simple algorithm and easy to implement. However, the traditional PSO’s have two primary issues: premature convergence and loss of diversity. These problems arise at the latter stages of the evolution process when dealing with high-dimensional, complex and electromagnetic inverse problems. To address these types of issues in the PSO approach, we proposed an Improved PSO (IPSO) which employs a dynamic control parameter as well as an adaptive mutation mechanism. The main proposal of the novel adaptive mutation operator is to prevent the diversity loss of the optimization process while the dynamic factor comprises the balance between exploration and exploitation in the search domain. The experimental outcomes achieved by solving complicated and extremely high-dimensional optimization problems were also validated on superconducting magnetic energy storage devices (SMES). According to numerical and experimental analysis, the IPSO delivers a better optimal solution than the other solutions described, particularly in the early computational evaluation of the generation.

Suggested Citation

  • Rehan Ali Khan & Shiyou Yang & Shafiullah Khan & Shah Fahad & Kalimullah, 2021. "A Multimodal Improved Particle Swarm Optimization for High Dimensional Problems in Electromagnetic Devices," Energies, MDPI, vol. 14(24), pages 1-19, December.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8575-:d:706258
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/24/8575/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/24/8575/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohd Nadhir Ab Wahab & Samia Nefti-Meziani & Adham Atyabi, 2015. "A Comprehensive Review of Swarm Optimization Algorithms," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-36, May.
    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. Memon, Mudasir Ahmed & Mekhilef, Saad & Mubin, Marizan & Aamir, Muhammad, 2018. "Selective harmonic elimination in inverters using bio-inspired intelligent algorithms for renewable energy conversion applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2235-2253.
    2. Sangeeta & Kapil Sharma & Manju Bala, 2020. "An ecological space based hybrid swarm-evolutionary algorithm for software reliability model parameter estimation," 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. 11(1), pages 77-92, February.
    3. Hormozi, Elham & Hu, Shuwen & Ding, Zhe & Tian, Yu-Chu & Wang, You-Gan & Yu, Zu-Guo & Zhang, Weizhe, 2022. "Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation," Energy, Elsevier, vol. 252(C).
    4. Minfang Huang & Qiong Guo & Jing Liu & Xiaoxu Huang, 2018. "Mixed Model Assembly Line Scheduling Approach to Order Picking Problem in Online Supermarkets," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
    5. Himansu Das & Sanjay Prajapati & Mahendra Kumar Gourisaria & Radha Mohan Pattanayak & Abdalla Alameen & Manjur Kolhar, 2023. "Feature Selection Using Golden Jackal Optimization for Software Fault Prediction," Mathematics, MDPI, vol. 11(11), pages 1-28, May.
    6. Shah Fahad & Shiyou Yang & Rehan Ali Khan & Shafiullah Khan & Shoaib Ahmed Khan, 2021. "A Multimodal Smart Quantum Particle Swarm Optimization for Electromagnetic Design Optimization Problems," Energies, MDPI, vol. 14(15), pages 1-11, July.
    7. Anupama Kaushik & Shivi Verma & Harsh Jot Singh & Gitika Chhabra, 2017. "Software cost optimization integrating fuzzy system and COA-Cuckoo optimization 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. 8(2), pages 1461-1471, November.
    8. Kharkeshi, Behrad Alizadeh & Shafaghat, Rouzbeh & Jahanian, Omid & Alamian, Rezvan & Rezanejad, Kourosh, 2022. "Experimental study of an oscillating water column converter to optimize nonlinear PTO using genetic algorithm," Energy, Elsevier, vol. 260(C).
    9. Khamis, Nurulaqilla & Selamat, Hazlina & Ismail, Fatimah Sham & Lutfy, Omar Farouq & Haniff, Mohamad Fadzli & Nordin, Ili Najaa Aimi Mohd, 2020. "Optimized exit door locations for a safer emergency evacuation using crowd evacuation model and artificial bee colony optimization," Chaos, Solitons & Fractals, Elsevier, vol. 131(C).
    10. Islam, Towhidul & Meade, Nigel & Carson, Richard T. & Louviere, Jordan J. & Wang, Juan, 2022. "The usefulness of socio-demographic variables in predicting purchase decisions: Evidence from machine learning procedures," Journal of Business Research, Elsevier, vol. 151(C), pages 324-338.
    11. Mustafa Erkan Turan, 2016. "Fuzzy Systems Tuned By Swarm Based Optimization Algorithms for Predicting Stream flow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4345-4362, September.
    12. De Vincenzo, Ilario & Massari, Giovanni F. & Giannoccaro, Ilaria & Carbone, Giuseppe & Grigolini, Paolo, 2018. "Mimicking the collective intelligence of human groups as an optimization tool for complex problems," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 259-266.
    13. Afroz Alam & Preeti Verma & Mohd Tariq & Adil Sarwar & Basem Alamri & Noore Zahra & Shabana Urooj, 2021. "Jellyfish Search Optimization Algorithm for MPP Tracking of PV System," Sustainability, MDPI, vol. 13(21), pages 1-20, October.
    14. Hossein Hassani & Mohammad Reza Yeganegi & Xu Huang, 2021. "Fusing Nature with Computational Science for Optimal Signal Extraction," Stats, MDPI, vol. 4(1), pages 1-15, January.
    15. Mohammad Javad Amoshahy & Mousa Shamsi & Mohammad Hossein Sedaaghi, 2016. "A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-42, August.
    16. Hilkija Gaïus Tosso & Saulo Anderson Bibiano Jardim & Rafael Bloise & Max Mauro Dias Santos, 2022. "Spark Ignition Engine Modeling Using Optimized Artificial Neural Network," Energies, MDPI, vol. 15(18), pages 1-23, September.
    17. Shafiq Ahmad, 2022. "Electromagnetic Field Optimization Based Selective Harmonic Elimination in a Cascaded Symmetric H-Bridge Inverter," Energies, MDPI, vol. 15(20), pages 1-18, October.
    18. Ming Liu & Yang Xu & Abdul-Wahid Mohammed, 2016. "Decentralized Opportunistic Spectrum Resources Access Model and Algorithm toward Cooperative Ad-Hoc Networks," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-21, January.

    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:jeners:v:14:y:2021:i:24:p:8575-:d:706258. 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.