Multi Unmanned Aerial Vehicle Area Coverage Control Based on Enhanced Alpha-Guided Grey Wolf Optimizer

With the development of multi unmanned aerial vehicle (UAV) technology, multi UAV is more and more widely used in intelligent agriculture, post disaster emergency communication, forest fire prevention and other fields. Coverage monitoring of target area is an important topic in the field of multi UAV research. Through the research on multi UAV communication coverage network, this paper analyzes the factors affecting multi UAV coverage and extracts the mathematical model of maximum coverage under the condition of ensuring UAV connectivity. Grey wolf optimizer (GWO) is a recently developed swarm intelligence algorithm, which has good performance in solving unknown and challenge space problems. In order to accelerate the convergence speed of the GWO and solve the multi UAV deployment problem, this paper proposed an enhanced alpha-guided GWO (EAgGWO). In EAgGWO, the evolving process of the best three individuals are guided by the update direction of best individual. The compared experiment with other three algorithms verifies the effectiveness of the proposed method.