Single- and multi-objective airfoil design using genetic algorithms and artificial intelligence

Transonic airfoil design problems are solved using a Genetic Algorithm (GA) based optimizer. At the desired operating point, the minimum drag and constant lift targets are achieved through either a scalarized objective function, involving an arbitrary weighting factor, or the Pareto technique. For the optimization of an airfoil at two operating points, similar approaches are used. The CPU cost of the optimizer is kept low through Arti-cial Intelligence. A multilayer perceptron is trained using already evaluated individuals and provides good, though approximate, tness predictions. With the regularly trained network, the direct ow solver calls are noticeably reduced