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

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 betwee...

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Bibliographic Details
Published in:Energies (Basel) 2020-01, Vol.13 (1), p.16
Main Authors: Sánchez-Chica, Ander, Zulueta, Ekaitz, Teso-Fz-Betoño, Daniel, Martínez-Filgueira, Pablo, Fernandez-Gamiz, Unai
Format: Article
Language:English
Subjects:
Air jets
Air temperature
Algorithms
Animal behavior
Artificial intelligence
Cooling
Cost function
Design optimization
Efficiency
Evolutionary algorithms
Food
Genetic algorithms
Heat
Heuristic
Jet impingement
Learning theory
Neighborhoods
Neural networks
Optimization
Pollination
Population
Researchers
Reynolds number
Variables
Velocity
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13010016