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ANN-Based Stop Criteria for a Genetic Algorithm Applied to Air Impingement Design

Author

Listed:
  • Ander Sánchez-Chica

    (System Engineering & Automation Control Department, University of the Basque Country, UPV/EHU, Nieves Cano 12, 01016 Vitoria-Gasteiz, Spain)

  • Ekaitz Zulueta

    (System Engineering & Automation Control Department, University of the Basque Country, UPV/EHU, Nieves Cano 12, 01016 Vitoria-Gasteiz, Spain)

  • Daniel Teso-Fz-Betoño

    (System Engineering & Automation Control Department, University of the Basque Country, UPV/EHU, Nieves Cano 12, 01016 Vitoria-Gasteiz, Spain)

  • Pablo Martínez-Filgueira

    (CS Centro Stirling S. Coop., Avenida Álava 3, 20550 Aretxabaleta, Spain)

  • Unai Fernandez-Gamiz

    (Nuclear Engineering & Fluid Mechanics Department, University of the Basque Country, UPV/EHU, Nieves Cano 12, 01016 Vitoria-Gasteiz, Spain)

Abstract

Artificial Neural Networks (ANNs) have proven to be a powerful tool in many fields of knowledge. At the same time, evolutionary algorithms show a very efficient technique in optimization tasks. Historically, ANNs are used in the training process of supervising networks by decreasing the error between the output and the target. However, we propose another approach in order to improve these two techniques together. The ANN is trained with the points obtained during an optimization process by a genetic algorithm and a flower pollination algorithm. The performance of this ANN is used as a stop criterion for the optimization process. This new configuration aims to reduce the number of iterations executed by the genetic optimizer when learning the cost function by an ANN. As a first step, this approach is tested with eight benchmark functions. As a second step, the authors apply it to an air jet impingement design process, optimizing the surface temperature and the fan efficiency. Finally, a comparison between the results of a regular optimization and the results obtained in the present study is presented.

Suggested Citation

  • Ander Sánchez-Chica & Ekaitz Zulueta & Daniel Teso-Fz-Betoño & Pablo Martínez-Filgueira & Unai Fernandez-Gamiz, 2019. "ANN-Based Stop Criteria for a Genetic Algorithm Applied to Air Impingement Design," Energies, MDPI, vol. 13(1), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:13:y:2019:i:1:p:16-:d:299564
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    References listed on IDEAS

    as
    1. Joanna Ferdyn-Grygierek & Krzysztof Grygierek, 2017. "Multi-Variable Optimization of Building Thermal Design Using Genetic Algorithms," Energies, MDPI, vol. 10(10), pages 1-20, October.
    2. Wieczorek, Maciej & Lewandowski, Mirosław, 2017. "A mathematical representation of an energy management strategy for hybrid energy storage system in electric vehicle and real time optimization using a genetic algorithm," Applied Energy, Elsevier, vol. 192(C), pages 222-233.
    3. Pablo Martínez-Filgueira & Ekaitz Zulueta & Ander Sánchez-Chica & Unai Fernández-Gámiz & Josu Soriano, 2019. "Multi-Objective Particle Swarm Based Optimization of an Air Jet Impingement System," Energies, MDPI, vol. 12(9), pages 1-16, April.
    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.
    5. Ehsan Gholamalizadeh & Man-Hoe Kim, 2016. "Multi-Objective Optimization of a Solar Chimney Power Plant with Inclined Collector Roof Using Genetic Algorithm," Energies, MDPI, vol. 9(11), pages 1-14, November.
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