IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i15p4613-d604783.html
   My bibliography  Save this article

A Multimodal Smart Quantum Particle Swarm Optimization for Electromagnetic Design Optimization Problems

Author

Listed:
  • Shah Fahad

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

  • Shiyou Yang

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

  • Rehan Ali Khan

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

  • Shafiullah Khan

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

  • Shoaib Ahmed Khan

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

Abstract

Electromagnetic design problems are generally formulated as nonlinear programming problems with multimodal objective functions and continuous variables. These can be solved by either a deterministic or a stochastic optimization algorithm. Recently, many intelligent optimization algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC), have been proposed and applied to electromagnetic design problems with promising results. However, there is no universal algorithm which can be used to solve engineering design problems. In this paper, a stochastic smart quantum particle swarm optimization (SQPSO) algorithm is introduced. In the proposed SQPSO, to tackle the premature convergence problem in order to improve the global search ability, a smart particle and a memory archive are adopted instead of mutation operations. Moreover, to enhance the exploration searching ability, a new set of random numbers and control parameters are introduced. Experimental results validate that the adopted control policy in this work can achieve a good balance between exploration and exploitation. Finally, the SQPSO has been tested on well-known optimization benchmark functions and implemented on the electromagnetic TEAM workshop problem 22. The simulation result shows an outstanding capability of the proposed algorithm in speeding convergence compared to other algorithms.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4613-:d:604783
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/15/4613/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/15/4613/
    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.
    2. Tiezhou Wu & Xiao Shi & Li Liao & Chuanjian Zhou & Hang Zhou & Yuehong Su, 2019. "A Capacity Configuration Control Strategy to Alleviate Power Fluctuation of Hybrid Energy Storage System Based on Improved Particle Swarm Optimization," Energies, MDPI, vol. 12(4), pages 1-11, February.
    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. Sergey Obukhov & Ahmed Ibrahim & Mohamed A. Tolba & Ali M. El-Rifaie, 2019. "Power Balance Management of an Autonomous Hybrid Energy System Based on the Dual-Energy Storage," Energies, MDPI, vol. 12(24), pages 1-15, December.
    2. 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.
    3. Si, Yupeng & Wang, Rongjie & Zhang, Shiqi & Zhou, Wenting & Lin, Anhui & Zeng, Guangmiao, 2022. "Configuration optimization and energy management of hybrid energy system for marine using quantum computing," Energy, Elsevier, vol. 253(C).
    4. 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.
    5. Min Yi & Wei Xie & Li Mo, 2021. "Short-Term Electricity Price Forecasting Based on BP Neural Network Optimized by SAPSO," Energies, MDPI, vol. 14(20), pages 1-17, October.
    6. 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).
    7. 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.
    8. 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.
    9. 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.
    10. 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).
    11. 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).
    12. 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.
    13. Shuang Lei & Yu He & Jing Zhang & Kun Deng, 2023. "Optimal Configuration of Hybrid Energy Storage Capacity in a Microgrid Based on Variational Mode Decomposition," Energies, MDPI, vol. 16(11), pages 1-19, May.
    14. Hao Ma & Jian Pan & Lei Lv & Guanghui Xu & Feng Ding & Ahmed Alsaedi & Tasawar Hayat, 2019. "Recursive Algorithms for Multivariable Output-Error-Like ARMA Systems," Mathematics, MDPI, vol. 7(6), pages 1-18, June.
    15. Lucrezia Manservigi & Mattia Cattozzo & Pier Ruggero Spina & Mauro Venturini & Hilal Bahlawan, 2020. "Optimal Management of the Energy Flows of Interconnected Residential Users," Energies, MDPI, vol. 13(6), pages 1-21, March.
    16. 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.
    17. Zhong Guan & Hui Wang & Zhi Li & Xiaohu Luo & Xi Yang & Jugang Fang & Qiang Zhao, 2024. "Multi-Objective Optimal Scheduling of Microgrids Based on Improved Particle Swarm Algorithm," Energies, MDPI, vol. 17(7), pages 1-20, April.
    18. Junxia Ma & Qiuling Fei & Fan Guo & Weili Xiong, 2019. "Variational Bayesian Iterative Estimation Algorithm for Linear Difference Equation Systems," Mathematics, MDPI, vol. 7(12), pages 1-16, November.
    19. Feng Ding & Jian Pan & Ahmed Alsaedi & Tasawar Hayat, 2019. "Gradient-Based Iterative Parameter Estimation Algorithms for Dynamical Systems from Observation Data," Mathematics, MDPI, vol. 7(5), pages 1-15, May.
    20. Zhenxing Zhao & Kaijie Chen & Ying Chen & Yuxing Dai & Zeng Liu & Kuiyin Zhao & Huan Wang & Zishun Peng, 2021. "An Ultra-Fast Power Prediction Method Based on Simplified LSSVM Hyperparameters Optimization for PV Power Smoothing," Energies, MDPI, vol. 14(18), pages 1-15, September.

    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:15:p:4613-:d:604783. 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.