HCA-IDGWO: a Joint Charging Optimization Algorithm for Wireless Rechargeable Sensor Networks

The combination of Wireless Rechargeable Sensor Networks (WRSNs) and charging Unmanned Aerial Vehicles (UAVs) presents broad prospects for applications in providing sustainable energy replenishment for sensors. How to deploy UAVs for WRSNs of different scales and design optimal collaborative charging trajectory to improve charging efficiency across the entire system at the lowest energy cost remains a challenge. In this work, we simultaneously investigate the partitioned charging strategy for WRSNs, the deployment of UAVs and their charging trajectory planning. Specifically, we develop multi-UAV collaborative charging schemes for both small-scale and large-scale WRSNs and propose a joint optimization algorithm: Hybrid Clustering Analysis and Improved Discrete Grey Wolf Optimization (HCA-IDGWO). HCA divides WRSNs into several subnetworks for partitioned charging based on the distribution of sensors. This approach facilitates energy balance and replenishment in large-scale WRSNs and helps determine the number of UAVs deployed in each network. In IDGWO, we employ an efficient encoding representation to discretize the Grey Wolf Optimizer (GWO) and use the Crossover Operator to update individuals while providing significant diversity to the population, enhancing global search capability. Additionally, we introduce a local search technique, Large Neighborhood Search (LNS), which uses Destroy and Repair Operators to further improve the quality of trajectory solutions, achieving an ideal balance between exploration and exploitation. Extensive experimental results demonstrate that HCA-IDGWO exhibits excellent performance in terms of precision, robustness and convergence speed, while also ensuring fairness in the assignment of UAVs’ workloads. Furthermore, the proposed algorithm shows strong competitiveness in maintaining the shortest total charging distance of all UAVs. Finally, we validate the feasibility and effectiveness of HCA-IDGWO on large-scale WRSNs. The charging schemes formulated by HCA-IDGWO effectively reduce the distance of UAVs’ charging trajectories, lower their energy consumption during flights, and improve energy utilization efficiency of the whole system, facilitating efficient collaboration between UAVs and maintaining the stable operation of WRSNs.