Optimization of Flight Endurance for Turboprop Air Taxis Using Metaheuristic Algorithms

This study aims to optimize the flight endurance of a 12-passenger turboprop air taxi using two metaheuristic optimization algorithms: Grey Wolf Optimization (GWO) and Ant Colony Optimization (ACO). Initially, the gradient descent method was employed to estimate the aircraft's maximum weight. Subsequently, the aircraft's performance characteristics were utilized as design variables and flight endurance was optimized under specific constraints without altering the physical structure of the aircraft. The optimization process was implemented, and the results were evaluated and compared in terms of performance and efficiency. This research demonstrated that the two mentioned algorithms, utilizing random and collective strategies, were able to enhance the aircraft's efficiency. Additionally, the optimization of flight endurance for three real aircraft—Piper, Beechcraft, and Bombardier—was examined compared to their original endurance. In this context, the Ant Colony Optimization algorithm exhibited better performance than the Grey Wolf Optimization algorithm, which could have a positive impact on flight operations without refueling or the process of finding alternative airports.