ACO-DTSP Algorithm: Optimizing UAV Swarm Routes with Workload Constraints.

In this research, we present a variant of the Ant Colony Optimization (ACO) algorithm tailored for optimizing routing within a limited-capacity environment for a swarm of drones. Our primary objective is to minimize task completion time while ensuring that individual node capacity constraints are never breached. Our algorithm leverages both pheromone trails and heuristic information to guide drone movements intelligently, and it dynamically updates the pheromone trail based on solution quality. This research underscores the importance of ideal routing algorithms in drone swarm systems and showcases ACO's effectiveness in optimizing such challenges. We conducted a comprehensive performance comparison against the existing methods like Christofides algorithm, Simulated Annealing, K-Opt and Lin-Kernighan Heuristic and the Brute-force method. Results indicate that our algorithm significantly improves time efficiency and is feasible for real-time environments. Furthermore, we highlight the potential for substantial enhancements in the system by deploying suitable hardware, such as quadcopters with specific capabilities. [ABSTRACT FROM AUTHOR]