A Comparative Study of Ant Colony Optimization and Genetic Algorithm for Path Planning of Aerial Food Delivery System

Abstract

As technology rules the world, drones are used for creative and valuable applications. Apart from the military applications, the usage of drones in civil domains has gained immense popularity in recent days. Emerging and re-emerging viral pathogens continue to pose a major threat to global public health. As one of the prevention method is to maintain a safe distance from others, contact less delivery using drones becomes more relevant. This paper concentrates on the parcel delivery system using drones. Unmanned Aerial Vehicles can replace the conventional manual food delivery system effectively while consuming less time. The main challenge for parcel delivery drones is to plan the optimal path meeting several constraints. In the current work, the path planning of delivery drones is formulated as a travelling sales man problem and solved with two different algorithms-Ant Colony Optimization and Genetic Algorithm. Comparison is done for analyzing the efficiency and performances of both algorithms in the case of aerial food delivery system.