###### Abstract
However, the two methods discussed above indicate modification in an existing airfoil design, and to achieve better and new designs through local airfoil modifications, different parameterization techniques can be applied. This airfoil shape can be modified through smooth analysis of the original airfoil coordinates through a shape analytical function (e.g., Bernstein polynomials or Legendre) [13]. ASO includes airfoil shape parameterization, optimization algorithms and airfoil design analysis. Optimization of the shape parameterization method has a great impact on the results and has the potential to accommodate a wider range of potential airfoil shapes with fewer design parameters in the design space [9]. Different aerodynamic shape parameterization techniques have been developed for the airfoil design and its parameterization [14]. There are several parametric methods for ASO, such as the parametric section (PASEC) method, B-spline, Bezier curves and class shape transformation (CST), which are widely used to fit the size of the airfoil through interpolation methods. Hicks-Henne parameterizations, CST methods, Bezier curves and B-spline curves are useful for reconstructing the airfoil, but the relative position of the control point is difficult to manage [15; 16]. [PERSON] and [PERSON] [17] reported in their work that analytical functions can be represented by airfoil families [18]. The results produced from the analytical functions cannot be used as an essential new design concept, but they are robust enough to represent different types of airfoils. The airfoil shape is represented by parametric methods with different basis functions because the parameters used in the airfoil are greater in number, making it possible to find better design results [19]. On the other hand, due to the increase in design variables, this results in the problem of finding a corrective algorithm that does not help to find the optimal design. Airfoil optimization plays an important role in maximizing the coefficient of lift (\(C_{l}\)), so the aerodynamic performance is improved, achieving benefits to the lift-to-drag (\(L\)/\(D\)) ratio and better endurance. During the optimization process, the airfoil shape is changed in every iteration to obtain the desired results. However, it is too difficult to use all sets of design variables for the optimization process. Therefore, the design variables should be limited from a nearly infinite amount to a finite set, and the parameterization method is designed to represent a completely new airfoil or an existing airfoil [20]. [PERSON] [15] and [PERSON] et al. [16] conducted a survey and confirmed that the parameterization method has a great influence on the whole optimization process. [PERSON] and [PERSON] [20] suggested that the polynomial approach based on parameterization schemes strongly influences the maximum design variables obtained as a result of the optimization. Some physically intuitive methods facilitate the use of airfoil parameters to explain its shapes (e.g., the trailing edge angle, the airfoil thickness or the radius of the leading edge). [PERSON] [21] presented a parameterization method called PARSEC. This method uses a total of 11 basic parameters to represent an airfoil shape. The parameters used in the PARSEC method are directly linked to the airfoil geometry (e.g., thickness, curvature, maximum thickness and abscess), and they give the designer a real idea of what the design will be. The geometry definition must be coupled with an optimization technique that must properly take the airfoil parameterization into consideration. In this work, an optimization process for airfoil geometry is introduced. This method is based on the genetic algorithm (GA) optimization method, finding the optimum results by the coupling parameterization method and producing the maximum lift-to-drag (_L/D_) ratio. The results are then compared to the CST parameterization method and the CFD results.