ON NOVEL USAGE OF A HYBRID METHOD (ANN and GA) FOR FASTER 3-D AERODYNAMIC OPTIMIZATION

Abstract

The purpose of this study is to offer a more efficient hybrid aerodynamic optimization method for 3-D wing configurations by using both genetic and artificial neural network. Artificial Neural Network (ANN) is used with a new approach in the aerodynamic optimization of a forward swept wing. The developed technique has been found much more robust than Genetic Algorithm (GA) only methods. For example, the new hybrid technique acquires the same fitness level as the one that GA only method can reach in 500 calculations, in about half time (about 250 calculations). The drag coefficient reduction is calculated %33 faster in the offered method. The neural network is embedded into the genetic algorithm along with augmented elitism to prevent possible bad members in the generations.