IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i4p427-d503317.html
   My bibliography  Save this article

Differential Evolution Optimal Parameters Tuning with Artificial Neural Network

Author

Listed:
  • Manu Centeno-Telleria

    (System Engineering and Automation Control Department, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain)

  • Ekaitz Zulueta

    (System Engineering and Automation Control Department, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain)

  • Unai Fernandez-Gamiz

    (Nuclear Engineering and Fluid Mechanics Department, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain)

  • Daniel Teso-Fz-Betoño

    (System Engineering and Automation Control Department, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain)

  • Adrián Teso-Fz-Betoño

    (System Engineering and Automation Control Department, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain)

Abstract

Differential evolution (DE) is a simple and efficient population-based stochastic algorithm for solving global numerical optimization problems. DE largely depends on algorithm parameter values and search strategy. Knowledge on how to tune the best values of these parameters is scarce. This paper aims to present a consistent methodology for tuning optimal parameters. At the heart of the methodology is the use of an artificial neural network (ANN) that learns to draw links between the algorithm performance and parameter values. To do so, first, a data-set is generated and normalized, then the ANN approach is performed, and finally, the best parameter values are extracted. The proposed method is evaluated on a set of 24 test problems from the Black-Box Optimization Benchmarking (BBOB) benchmark. Experimental results show that three distinct cases may arise with the application of this method. For each case, specifications about the procedure to follow are given. Finally, a comparison with four tuning rules is performed in order to verify and validate the proposed method’s performance. This study provides a thorough insight into optimal parameter tuning, which may be of great use for users.

Suggested Citation

  • Manu Centeno-Telleria & Ekaitz Zulueta & Unai Fernandez-Gamiz & Daniel Teso-Fz-Betoño & Adrián Teso-Fz-Betoño, 2021. "Differential Evolution Optimal Parameters Tuning with Artificial Neural Network," Mathematics, MDPI, vol. 9(4), pages 1-20, February.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:4:p:427-:d:503317
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/4/427/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/4/427/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tsafarakis, Stelios & Zervoudakis, Konstantinos & Andronikidis, Andreas & Altsitsiadis, Efthymios, 2020. "Fuzzy self-tuning differential evolution for optimal product line design," European Journal of Operational Research, Elsevier, vol. 287(3), pages 1161-1169.
    2. Biswas, Partha P. & Suganthan, P.N. & Wu, Guohua & Amaratunga, Gehan A.J., 2019. "Parameter estimation of solar cells using datasheet information with the application of an adaptive differential evolution algorithm," Renewable Energy, Elsevier, vol. 132(C), pages 425-438.
    3. Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
    4. Aitor Saenz-Aguirre & Ekaitz Zulueta & Unai Fernandez-Gamiz & Javier Lozano & Jose Manuel Lopez-Guede, 2019. "Artificial Neural Network Based Reinforcement Learning for Wind Turbine Yaw Control," Energies, MDPI, vol. 12(3), pages 1-17, January.
    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. Mokshin, Anatolii V. & Khabibullin, Roman A., 2022. "Is there a one-to-one correspondence between interparticle interactions and physical properties of liquid?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    2. Solomon Feleke & Raavi Satish & Balamurali Pydi & Degarege Anteneh & Almoataz Y. Abdelaziz & Adel El-Shahat, 2023. "Damping of Frequency and Power System Oscillations with DFIG Wind Turbine and DE Optimization," Sustainability, MDPI, vol. 15(6), pages 1-19, March.

    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. Konstantinos Zervoudakis & Stelios Tsafarakis, 2025. "Customer segmentation using flying fox optimization algorithm," Journal of Combinatorial Optimization, Springer, vol. 49(1), pages 1-20, January.
    2. Wang, Sen & Gao, Yi, 2021. "A literature review and citation analyses of air travel demand studies published between 2010 and 2020," Journal of Air Transport Management, Elsevier, vol. 97(C).
    3. Longbing Cao, 2021. "AI in Finance: Challenges, Techniques and Opportunities," Papers 2107.09051, arXiv.org.
    4. Senturk, Ali, 2020. "Investigation of datasheet provided temperature coefficients of photovoltaic modules under various sky profiles at the field by applying a new validation procedure," Renewable Energy, Elsevier, vol. 152(C), pages 644-652.
    5. Sarun Kamolthip, 2021. "Macroeconomic Forecasting with LSTM and Mixed Frequency Time Series Data," PIER Discussion Papers 165, Puey Ungphakorn Institute for Economic Research.
    6. Ma, Liang & Chen, Bin & Wang, Xiaodong & Zhu, Zhengqiu & Wang, Rongxiao & Qiu, Xiaogang, 2019. "The analysis on the desired speed in social force model using a data driven approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 894-911.
    7. Zhuo Liang & Pengkun Quan & Shichun Di, 2025. "Stiffness Regulation of Cable-Driven Redundant Manipulators Through Combined Optimization of Configuration and Cable Tension," Mathematics, MDPI, vol. 13(11), pages 1-24, May.
    8. Wenyong Zhang & Lingfei Li & Gongqiu Zhang, 2021. "A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface," Papers 2106.07177, arXiv.org, revised Jan 2022.
    9. Carlos Cárdenas-Bravo & Rodrigo Barraza & Antonio Sánchez-Squella & Patricio Valdivia-Lefort & Federico Castillo-Burns, 2021. "Estimation of Single-Diode Photovoltaic Model Using the Differential Evolution Algorithm with Adaptive Boundaries," Energies, MDPI, vol. 14(13), pages 1-24, June.
    10. Daeil Lee & Seoryong Koo & Inseok Jang & Jonghyun Kim, 2022. "Comparison of Deep Reinforcement Learning and PID Controllers for Automatic Cold Shutdown Operation," Energies, MDPI, vol. 15(8), pages 1-25, April.
    11. Fabian Waldow & Matthias Schnaubelt & Christopher Krauss & Thomas Günter Fischer, 2021. "Machine Learning in Futures Markets," JRFM, MDPI, vol. 14(3), pages 1-14, March.
    12. Prabodh Khampariya & Sidhartha Panda & Hisham Alharbi & Almoataz Y. Abdelaziz & Sherif S. M. Ghoneim, 2022. "Coordinated Design of Type-2 Fuzzy Lead–Lag-Structured SSSCs and PSSs for Power System Stability Improvement," Sustainability, MDPI, vol. 14(11), pages 1-21, May.
    13. Jesús Enrique Sierra-García & Matilde Santos, 2021. "Lookup Table and Neural Network Hybrid Strategy for Wind Turbine Pitch Control," Sustainability, MDPI, vol. 13(6), pages 1-17, March.
    14. Martin Ćalasan & Dražen Jovanović & Vesna Rubežić & Saša Mujović & Slobodan Đukanović, 2019. "Estimation of Single-Diode and Two-Diode Solar Cell Parameters by Using a Chaotic Optimization Approach," Energies, MDPI, vol. 12(21), pages 1-14, November.
    15. Grilli, Luca & Santoro, Domenico, 2020. "Generative Adversarial Network for Market Hourly Discrimination," MPRA Paper 99846, University Library of Munich, Germany.
    16. Amira Elkodama & Amr Ismaiel & A. Abdellatif & S. Shaaban & Shigeo Yoshida & Mostafa A. Rushdi, 2023. "Control Methods for Horizontal Axis Wind Turbines (HAWT): State-of-the-Art Review," Energies, MDPI, vol. 16(17), pages 1-32, September.
    17. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
    18. Mateusz Buczyński & Marcin Chlebus, 2021. "GARCHNet - Value-at-Risk forecasting with novel approach to GARCH models based on neural networks," Working Papers 2021-08, Faculty of Economic Sciences, University of Warsaw.
    19. Qais, Mohammed H. & Hasanien, Hany M. & Alghuwainem, Saad, 2019. "Identification of electrical parameters for three-diode photovoltaic model using analytical and sunflower optimization algorithm," Applied Energy, Elsevier, vol. 250(C), pages 109-117.
    20. Nestoras Chalkidis & Rahul Savani, 2021. "Trading via Selective Classification," Papers 2110.14914, arXiv.org, revised Oct 2021.

    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:9:y:2021:i:4:p:427-:d:503317. 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.