Elsherbiny A.M.Bayoumy A.M.Elshabka A.M.Abdelrahman M.M.Aeronautical DepartmentMilitary Technical CollegeCairoEgypt; Aeronatuical departmentMechatronics DepartmentMSA UniversityGizaEgypt; Aeronautical DepartmentCairo universityCairoEgypt2020-01-092020-01-0920189.78E+12https://doi.org/10.2514/6.2018-0429https://arc.aiaa.org/doi/abs/10.2514/6.2018-0429ScopusIn this paper a hybrid optimization method is introduced to convert the aerodynamic shape of a conventional aerial subsonic flying body into a glide one by providing a range extension kit and fins. The selections of configuration and airfoils are described depending on the tactical requirements and flight regimes. The wing and fins sizing is obtained using four different methods subjected to geometric constraints. The first method is an iterative optimization method using linear aerodynamic coefficients and derivatives. The second method is a multi-objective function genetic algorithm aims to maximize stability, controllability and lift-drag ratio within certain weights using linear aerodynamic data. The third method is a genetic algorithm optimization function integrated with MISSILE DATCOM aims to maximize lift-drag ratio. The fourth method is a hybrid optimization method that integrate MISSILE DATCOM with both genetic algorithm and gradient-based optimization method. Then perform a direct uncontrolled six degree of freedom simulation for the four designs and the conventional flying body. Comparing the results of ranges for these bodies reveals that the hybrid optimization method has the best range over the other designs including the conventional flying body. � 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.EnglishAerodynamic dragAerodynamic stabilityAerodynamicsAntennasAviationDegrees of freedom (mechanics)DragFins (heat exchange)Genetic algorithmsLiftLift drag ratioMissilesOptimizationVertical stabilizersGenetic-algorithm optimizationsGeometric constraintGradient-based optimization methodHybrid optimization methodIterative OptimizationMulti-objective functionsRange-extension kitSix degree-of-freedomIterative methodsConference Paperhttps://doi.org/10.2514/6.2018-0429