An Adaptive Fuzzy Quantum Behavior Particle Swarm Optimization Algorithm for Mobile Charging Scheduling in Wireless Rechargeable Sensor Networks

The utilization of mobile chargers equipped with Wireless Energy Transmission (WET) devices to charge sensors in a Wireless Rechargeable Sensor Network (WRSN) has emerged as a promising solution to address the energy constraints of the network. The charging performance of a Wireless Rechargeable Sensor Network (WRSN) relies heavily on the efficiency of the mobile charging scheduling (MCS) strategy. However, designing an effective MCS strategy to achieve efficient wireless energy replenishment poses a challenge. Existing work often assumes that sensors will not be recharged once their energy is depleted, neglecting the fact that sensors can recover after being charged in practical applications. This paper addresses the mobile charging sequence optimization scheduling problem in WRSNs, considering the recovery of failed sensors after being charged. Therefore, we propose an adaptive quantum particle swarm optimization algorithm based on a fuzzy control strategy (AFQPSO). The AFQPSO incrementally adjusts the Quadratic Parameter of Charging Timeliness about Network (QPCTN) using fuzzy reasoning. Experimental results demonstrate that the AFQPSO-MCS achieves a smaller QPCTN and exhibits superior global search ability.