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

Ander Sánchez-Chica, Ekaitz Zulueta, Daniel Teso-Fz-Betoño, Pablo Martínez-Filgueira and Unai Fernandez-Gamiz
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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

Energies, 2019, vol. 13, issue 1, 1-17

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.

Keywords: air jet impingement; artificial neural network; genetic algorithms; cooling enhancement; multi-objective optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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