2-OptACO: An Improvement of Ant Colony Optimization for UAV Path in Disaster Rescue

Unmanned aerial vehicle (UAV) is favored by the industry to search and locate the lost personss in mountains and the trapped personss in earthquakes, fires and other disasters because it is not limited by the obstruction on the ground. Currently, however, a UAV always searches and locates the targets along a fixed flight path, which consumes more time and has lower accuracy. This kind of method can only provide a rough position estimation. Guideloc takes the UAVs GPS coordinates as the location information of the target and the genetic algorithm (GA) is used for path planning in order to shorten the flight path to improve the search efficiency and obtain a good result. But its performance still has room for improvement. In this paper, the path optimization algorithm used in Guideloc was further discussed and studied, and then a 2-OptACO method was proposed. The method is based on the 2-opt algorithm to improve the ant colony optimization algorithm (ACO) and is applied to optimize the UAVs path for search and rescue. The simulation results show that the 2-OptACO method has a faster convergence rate than the GA and ACO. It can obtain a better global optimal solution.