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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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    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.
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    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.

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