Multi-Objective Optimization for Multi-UAV Enabled WRSNs with PADs

With the advancements in wireless power transfer technology, wireless rechargeable sensor networks (WRSNs) have emerged as a prominent research area. In the studies of WRSNs, there has been growing interest in using unmanned aerial vehicles (UAVs) as mobile chargers (MCs) to overcome geographical constraints. Recently, a fixed wireless charging station called the power access docker (PADs) have been introduced in the UAV-based WRSNs to extend the operating range of the UAV in WRSNs by recharging the UAV. However, existing studies on WRSNs with PADs are predominantly limited to single-UAV systems. Thus, this paper focuses on jointly designing the sensor allocation and flight path for a multi-UAV enabled WRSNs, where multiple PADs are integrated to replenish energy to UAVs during their mission of charging terrestrial sensors. The goal of this work is to simultaneously minimize the maximum task completion time and the total energy consumption of all dispatched UAVs. First, we propose a load balancing based insertion method (LBIM) combined with ant colony optimization (ACO) to generate a feasible initial solution set. This designed approach primarily handles the recharging decisions of UAVs under the energy depletion situation and ensures that each UAV will not drain out its battery before returning to the base station whereas balancing the workload among all UAVs. Next, we develop an innovative enhanced multi-objective adaptive large neighborhood search (EMO-ALNS) algorithm to iteratively optimize the sensor allocation for each UAV and the visiting order of sensors and PADs based on the feasible initial solution. Extensive simulation results validate the effectiveness and superiority of the proposed algorithm compared with exiting arts. Specifically, the maximum task completion time can be reduced by up to 20.8%, and the total energy consumption can be reduced by up to 5.9%.