Inverse design of airfoils via an intelligent hybrid optimization technique

The solutions for inverse shape design (ISD) problems are provided in many cases using evolutionary algorithms linked up with CFD solvers. Among the optimization methods, evolutionary algorithms have many advantages, especially in stability, but they usually need a large number of function evaluations. This can be more important as evaluation of cost function requires flow solution which is usually a time-consuming process. This paper presents a new population-based hybrid algorithm called genetic-based bees algorithm (GBBA) as a solution to ISD problems. This method uses crossover and neighborhood searching operators derived from, respectively, genetic algorithm (GA) and bees algorithm (BA) to provide a method with good performance in accuracy and speed convergence. Three test cases have been used to compare the performance of the proposed hybrid algorithm with GA and BA. Here, both ideal and viscose flow solvers are involved to solve flow equations in the physical domain. PARSEC and Bezier are two shape definition methods used for surface modification in these cases. The results show that the final shape obtained by the proposed hybrid algorithm is more accurate compared to either BA or GA. Furthermore, speed convergence increases when GBBA is employed.