Effective surrogate-assisted genetic algorithm for airfoil aerodynamic optimization design

We now present SAGA (Surrogate-Assisted Genetic Algorithm), an effective algorithm for airfoil aerodynamic optimization design. In the full paper, we explain SAGA and its effectiveness in airfoil aerodynamic design. In this abstract, we just add some pertinent remarks to the three topics of explanation. The first topic is: Kriging model used in SAGA. This model is taken from Ref 2 by J. Sack et al. The second topic is: SAGA. In the second topic, most of the individuals are evaluated by the timesaving surrogate model. Also in the second topic, we take the EI (Expected improvement) method in Ref 4 by R. J. Donald et al to select the calibration individuals effectively. The third topic is: airfoil aerodynamic optimization design. In the third topic, we take RAE2822 airfoil and optimize it with SAGA and with SGA (Simple Genetic Algorithm) respectively; the results, given in Table 1 in the full paper, show preliminarily that both SAGA and SGA can increase through optimization the lift-drag ratio of RAE 2822 airfoil by about 40%. Also in the third topic, we point out that the optimization time required by SAGA is only about 25% of that of SGA.