Effect Analysis of Adaptive Ant Colony Algorithm with QoS Constraints Applied in Drone Disaster Area Search and Rescue System

Abstract

With the development of technology, drones have been widely used in disaster area search and rescue. In the face of low efficiency and long time consumption in drone disaster area search and rescue, this study proposes an adaptive ant colony algorithm based on service quality constraints for drone search and rescue models. The model first constructs a disaster area drone search and rescue scenario, then introduces service quality constraints to optimize and improve the ant colony algorithm. Finally, Matlab R2017a is used for simulation experiments to test the algorithm's performance. The results showed that in the experiment in a 32 scale area, the proposed algorithm achieved a minimum travel dealer length of 1.55km, which was reduced by 0.05km and 0.01km respectively compared to the traditional ant colony algorithm's 1.60km and particle swarm optimization algorithm's 1.56km. In the 128 scale and 512 scale regions, the proposed algorithm exhibited shorter average path lengths and higher stability. The data shows that research algorithms have better search and rescue efficiency and quality in dynamically changing disaster environments.