Multi-Objective Deployment Optimization of UAVs for Energy-Efficient Wireless Coverage

Recently, Unmanned Aerial Vehicles (UAVs) have attracted much attention due to their flexibility and low cost. However, there are limitations for multiple UAVs such as limited energy and collaborative coverage. To achieve a better coverage performance, each UAV needs to find the optimal position to cover many ground users while saving energy. However, there are trade-offs between coverage utility and energy consumption. In this paper, we study a multi-UAV communication scenario where multi-UAV array is deployed to provide wireless coverage for mobile ground users. Considering the number, 3D positions, and speeds of UAVs, we formulate a Coverage Utility and Energy Multi-objective Optimization Problem (CUEMOP) to simultaneously maximize the total coverage utility and minimize the total energy consumption of UAVs. Due to the complexity and NP-hardness of the formulated CUEMOP, we propose Improved Multi-objective Grey Wolf Optimizer (ImMOGWO) algorithm. In this algorithm, we design the Role Determination (RD) algorithm to cluster the ground users and prepare for initialization of UAV number and position. Hybrid solution initialization (HSI) algorithm is to initialize multi-dimensional variables and overcome algorithm inefficiency caused by random initialization. The Levy flight and Sin Cosine method based on the MOGWO algorithm (LSCMGA) is proposed to increase the diversity of solutions and ensure the convergence effect of the algorithm. Simulation results verify that proposed ImMOGWO algorithm has better performance than some other benchmark methods.