Drag Minimization of Low Subsonic Airfoil with Constrained Genetic Algorithm - Dwianto, Yohanes Bimo; Pramutadi, Ardanto Mohammad (Faculty of Mechanical and Aerospace Engineering, 2023)
Drag minimization of low subsonic airfoil was conducted with constrained genetic algorithm (CGA). To cope with the constraints, each of these two different types of constraint handling techniques (CHTs), namely Superiority of Feasible Individual (SoF) and Generalized Multiple Constraint Ranking (G-MCR) were employed to the CGA and compared. From three independent runs for each CHT, it was obtained that G-MCR performed significantly better than SoF, indicating that G-MCR, a novel type of CHT, provides better exploration of the design space to obtain better solution. The obtained best airfoil designs were compared with a baseline airfoil and analyzed. The best optimum airfoil increases the aerodynamic efficiency by 21.4%. It was observed that the reduction of drag only occurs locally, so that a robust optimization is required in the future.