Multi-objective evolutionary optimization of subsonic airfoils by meta-modelling and evolution control


The current work concerns the application of multi-objective evolutionary optimization by approximation function to aerodynamic design. A new general technique, named evolution control (EC), is used in order to manage the on-line enriching of correct solutions database, which is the basis of the learning procedure for the approximators. Substantially, this approach provides that the database, initially quite small and enabling a very inaccurate approximation, should be integrated during the optimization. Such integration is done by means of some choice criteria, allowing deciding which individuals of the current population should be verified. The technique showed being efficacious and very efficient for the considered problem, whose dimensionality are 5. Even if general principle of EC is valid independently from the kind of adopted approximator, this last strongly affects the application. Obtained results are utilized to show how the adoption of artificial neural networks and kriging can differently influence the whole optimization process. Moreover, first results, achieved after reformulating the same problem with seven parameters, support the idea of the performance of the method scale well with dimensionality.