Recent advancement in wireless charging technologies has enabled us to design and development of Wireless Rechargeable Sensor Networks (WRSNs) for sensing and data gathering tasks for a very long duration. The fundamental research challenge in WRSN is to design efficient path scheduling for Mobile Wireless Charging Vehicles (MWCVs) such that it maximizes utility of energy resource of MWCVs and minimizes average delay in charging process of the network. Most of the existing solutions for path scheduling of MWCVs suffer from high charging latency,poor energy usage efficiency, and low scalability issues. In order to overcome these issues, this research paper proposed a novel algorithm for scheduling of multiple mobile rechargers using Hybrid meta-heuristic technique. In the proposed Hybrid meta-heuristic-based algorithm, best features of Cuckoo Search and Genetic Algorithm are combined to optimize the path scheduling problem. This work derives a novel fitness function for optimizing the performance of the scheduling. To show the effectiveness of the proposed scheme, an extensive simulation experiments are performed under different network scenarios and results are compared with the latest state-of-art schemes. Result analysis confirms advantages of the proposed scheme in terms of charging latency, total travel distance and energy usage efficiency.

无线充电技术的最新发展使我们能够设计和开发用于很长时间的传感和数据收集任务的无线可充电传感器网络（WRSN）。WRSN的根本研究挑战是为移动无线充电车（MWCV）设计有效的路径调度，以使其最大化MWCV的能源利用率，并最小化网络充电过程中的平均延迟。MWCV的路径调度的大多数现有解决方案遭受充电延迟高，能源使用效率低和可扩展性低的问题。为了克服这些问题，本研究提出了一种使用混合元启发式技术调度多个移动充电器的新算法。在提出的基于混合元启发式算法的混合算法中，结合了布谷鸟搜索和遗传算法的最佳功能来优化路径调度问题。这项工作得出了一种用于优化调度性能的新颖适应性函数。为了显示所提出方案的有效性，在不同的网络情况下进行了广泛的仿真实验，并将结果与​​最新的最新方案进行了比较。结果分析证实了该方案在充电等待时间，总行驶距离和能源使用效率方面的优势。