ant colony optimization algorithm

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.

Searching and optimizing by using collective intelligence are known as highly efficient methods that can be used to solve complex engineering problems. Ant colony optimization algorithm (ACO) is based on collective intelligence inspired by ants' behavior in finding the best path in search of food. In this paper, the ACO algorithm is used for image edge detection. A fuzzy-based system is proposed to increase the dynamics and speed of the proposed method. This system controls the amount of pheromone and distance. Thus, instead of considering constant values for the parameters of the algorithm, variable values are used to make the search space more accurate and reasonable. The fuzzy ant colony optimization algorithm is applied on several images to illustrate the performance of the proposed algorithm. The obtained results show better quality in extracting edge pixels by the proposed method compared to several image edge detection methods. The improvement of the proposed method is shown quantitatively by the investigation of the time and entropy of conventional methods and previous works. Also, the robustness of the proposed method is demonstrated against additive noise.