A Novel Hybrid Discrete Grey Wolf Optimizer Algorithm for Multi-UAV Path Planning

With the development of the fifth-generation wireless network, autonomous moving platforms such as unmanned aerial vehicles (UAV) have been widely used in modern smart cities. In some applications, the UAVs need to perform certain monitoring tasks within a specified time. However, due to the energy constraints of UAVs, such tasks require using multiple UAVs to monitor multiple points. To solve this practical problem, this paper proposes a multi-UAV path planning model with the energy constraint (MUPPEC). The MUPPEC considers the energy consumption of a UAV in different states, such as acceleration, cruising speed, deceleration, and hovering, and the main objective of the MUPPEC is to minimize the total monitoring time. Also, a hybrid discrete intelligence algorithm based on the grey wolf optimizer (HDGWO) is proposed to solve the MUPPEC. In the HDGWO, the discrete grey wolf update operators are implemented, and the integer coding and greedy algorithms are used to transform between the grey wolf space and discrete problem space. Furthermore, the central position operation and stagnation compensation grey wolf update operation are introduced to improve the global convergence ability, and a two-opt with azimuth is designed to enhance the local search ability of the algorithm. Experimental results show that the HDGWO can solve the MUPPEC effectively, and compared to the traditional grey wolf optimizer(GWO), the discrete operators and the two-opt local search strategy with azimuth can effectively improve the optimization ability of the GWO.