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The main disadvantage of evolutionary algorithms for airfoil design problems is the high computational costs associated with the use of computational fluid dynamics solvers. This disadvantage was significantly reduced by using an existing technique which combines a genetic algorithm and a neural network to rapidly improve populations. In this technique, both genetic algorithms and a properly trained neural network search the design space, resulting in an interactive process between a genetic algorithm and a neural network that greatly improves the exploration power of the algorithm. Experimental results show that the implemented algorithm discovers the desired solution quickly and significantly reduces the overall costs of solving the problem with little or no reduction in robustness.

Within this study, it is aimed to provide an efficient algorithm for inverse design of 2-D airfoil in different flow conditions. For this purpose, as a stochastic search method, current vibrational genetic algorithm (VGA) is improved and used to accelerate the algorithm for inverse design. From the results obtained, it is concluded that VGA decreased the required time for optimal airfoil solution beside its simplicity. Low population rate and short generation cycle are the main benefits of vibrational genetic algorithm. [ABSTRACT FROM AUTHOR]