Comparison of ACO and GA techniques to generate Neural Network based Bezier-PARSEC parameterized airfoil

This research uses Neural Networks to determine two dimensional airfoil geometry using Bezier-PARSEC parameterization. Earlier, Ant Colony Optimization (ACO) techniques have been used to solve combinatorial optimization problems like TSP. This work extends ACO method from TSP problem to design parameters for estimating unknown Bezier-PARSEC parameters that define upper and lower curves of the airfoil. The efficiency and the performance of ACO technique was compared to that of GA. The work established that ACO exhibited improved performance than the GA in terms of optimization time and level of precision achieved. In the next phase, Neural Network is implemented using Cp as input in terms of Cl, Cd and Cm for learning and targeting the corresponding Bezier-PARSEC parameters. Neural Networks including Feed-forward back propagation, Generalized Regression and Radial Basis were implemented and were compared to evaluate their performance. Similar to earlier work with GA and Neural Nets, this work also established Feed-forward back propagation Neural Network as a preferred method for determining the design of airfoil since the technique presented better approximation results than other neural nets.