Multi-objective Evolutionary Optimization of Subsonic Airfoils

This work concerns the application of multi-objective evolutionary optimization by approximation function to 2D aerodynamic design. The new general concept of evolution control is used to on-line enriching the database of correct solutions, which are the basis of the learning procedure for the approximators. Substantially, given an initial very poor model approximation, which means small size of the database, the database, and consequently the models, is enriched by evaluating part of the individuals of the optimization process. The technique showed being very efficient for the considered problem, by requiring few hundreds of true computations when the dimensionality of the problem is 5. Obtained results are utilized to show how the adopted approximation techniques can influence the whole optimization process. For this work artificial neural networks and kriging approximators are used.