IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i20p4224-d1256395.html
   My bibliography  Save this article

Propagation Search Algorithm: A Physics-Based Optimizer for Engineering Applications

Author

Listed:
  • Mohammed H. Qais

    (Centre for Advances in Reliability and Safety, Hong Kong, China)

  • Hany M. Hasanien

    (Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt)

  • Saad Alghuwainem

    (Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Ka Hong Loo

    (Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, China)

Abstract

For process control in engineering applications, the fewer the coding lines of optimization algorithms, the more applications there are. Therefore, this work develops a new straightforward metaheuristic optimization algorithm named the propagation search algorithm (PSA), stirred by the wave propagation of the voltage and current along long transmission lines. The mathematical models of the voltage and current are utilized in modeling the PSA, where the voltage and current are the search agents. The propagation constant of the transmission line is the control parameter for the exploitation and exploration of the PSA. After that, the robustness of the PSA is verified using 23 famous testing functions. The statistical tests, comprising mean, standard deviation, and p -values, for 20 independent optimization experiments are utilized to confirm the robustness of the PSA to find the best result and the significant difference between the outcomes of the PSA and those of the compared algorithms. Finally, the proposed PSA is applied to find the optimum design parameters of four engineering design problems, including a three-bar truss, compression spring, pressure vessel, and welded beam. The outcomes show that the PSA converges to the best solutions very quickly, which can be applied to those applications that require a fast response.

Suggested Citation

  • Mohammed H. Qais & Hany M. Hasanien & Saad Alghuwainem & Ka Hong Loo, 2023. "Propagation Search Algorithm: A Physics-Based Optimizer for Engineering Applications," Mathematics, MDPI, vol. 11(20), pages 1-26, October.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:20:p:4224-:d:1256395
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/20/4224/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/20/4224/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohammed H. Qais & Hany M. Hasanien & Rania A. Turky & Saad Alghuwainem & Marcos Tostado-Véliz & Francisco Jurado, 2022. "Circle Search Algorithm: A Geometry-Based Metaheuristic Optimization Algorithm," Mathematics, MDPI, vol. 10(10), pages 1-27, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shih-Cheng Horng & Shieh-Shing Lin, 2024. "Accelerated Driving-Training-Based Optimization for Solving Constrained Bi-Objective Stochastic Optimization Problems," Mathematics, MDPI, vol. 12(12), pages 1-19, June.

    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. Mohamed Abdel-Basset & Reda Mohamed & Karam M. Sallam & Ripon K. Chakrabortty, 2022. "Light Spectrum Optimizer: A Novel Physics-Inspired Metaheuristic Optimization Algorithm," Mathematics, MDPI, vol. 10(19), pages 1-63, September.
    2. 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.
    3. Mohamed A. M. Shaheen & Zia Ullah & Mohammed H. Qais & Hany M. Hasanien & Kian J. Chua & Marcos Tostado-Véliz & Rania A. Turky & Francisco Jurado & Mohamed R. Elkadeem, 2022. "Solution of Probabilistic Optimal Power Flow Incorporating Renewable Energy Uncertainty Using a Novel Circle Search Algorithm," Energies, MDPI, vol. 15(21), pages 1-19, November.
    4. Slim Abid & Ali M. El-Rifaie & Mostafa Elshahed & Ahmed R. Ginidi & Abdullah M. Shaheen & Ghareeb Moustafa & Mohamed A. Tolba, 2023. "Development of Slime Mold Optimizer with Application for Tuning Cascaded PD-PI Controller to Enhance Frequency Stability in Power Systems," Mathematics, MDPI, vol. 11(8), pages 1-32, April.
    5. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Khalid Mehmood Cheema & Muhammad Asif Zahoor Raja & Ahmad H. Milyani & Abdullah Ahmed Azhari, 2022. "Nonlinear Hammerstein System Identification: A Novel Application of Marine Predator Optimization Using the Key Term Separation Technique," Mathematics, MDPI, vol. 10(22), pages 1-22, November.
    6. Mehmood, Khizer & Chaudhary, Naveed Ishtiaq & Khan, Zeshan Aslam & Cheema, Khalid Mehmood & Raja, Muhammad Asif Zahoor & Shu, Chi-Min, 2023. "Novel knacks of chaotic maps with Archimedes optimization paradigm for nonlinear ARX model identification with key term separation," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    7. Malika Fodil & Ali Djerioui & Mohamed Ladjal & Abdelhakim Saim & Fouad Berrabah & Hemza Mekki & Samir Zeghlache & Azeddine Houari & Mohamed Fouad Benkhoris, 2023. "Optimization of PI Controller Parameters by GWO Algorithm for Five-Phase Asynchronous Motor," Energies, MDPI, vol. 16(10), pages 1-14, May.
    8. Khan, Taimoor Ali & Chaudhary, Naveed Ishtiaq & Khan, Zeshan Aslam & Mehmood, Khizer & Hsu, Chung-Chian & Raja, Muhammad Asif Zahoor, 2024. "Design of Runge-Kutta optimization for fractional input nonlinear autoregressive exogenous system identification with key-term separation," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    9. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Khalid Mehmood Cheema & Zeshan Aslam Khan & Muhammad Asif Zahoor Raja & Ahmad H. Milyani & Abdulellah Alsulami, 2023. "Design of Nonlinear Marine Predator Heuristics for Hammerstein Autoregressive Exogenous System Identification with Key-Term Separation," Mathematics, MDPI, vol. 11(11), pages 1-20, May.

    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:20:p:4224-:d:1256395. 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.