Multi-objective evolutionary optimization of subsonic airfoils by kriging approximation and evolution control

This work focuses on multi-objective evolutionary optimization by approximation function. It uses the new general concept of evolution control to online enriching the database of correct solutions, which are the basis of the learning procedure for the kriging approximators. Substantially, given an initial very poor model approximation (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 efficient for the considered aerodynamic problem, by requiring few hundreds of true computations when the dimensionality of the problem is 5.