Optimal Charger Deployment Strategy in Wrsns Using Modified Manta Ray Foraging Optimization

Wireless Rechargeable Sensor Networks (WRSNs) are a novel type of sensor network in which nodes are equipped with rechargeable batteries and wireless transceivers. Due to the limited energy of sensor nodes, the optimization deployment of chargers becomes a key issue to ensure the long-term stable operation of WRSNs. The quality of charger deployment directly affects the coverage and network lifetime of WRSNs. Therefore, how to deploy chargers reasonably in WRSNs to provide sufficient energy support for the network is a challenging research problem. This paper proposes an improved charger optimization deployment method based on the Manta Ray Foraging Optimization algorithm (PSMRFO), the original algorithm suffers from slow convergence speed and a tendency to get trapped in local optima. Therefore, this paper aims to enhance the algorithm's convergence speed and optimization accuracy by integrating various strategies, such as piecewise linear mapping, Levy flight, and splitting operator. Through a series of function validation experiments, the research results demonstrate a significant improvement in the convergence speed and optimization accuracy of the improved algorithm compared to the original one. Finally, the improved algorithm was applied to the charger deployment optimization problem and compared with LGMRFO, GPSOCD, and MRFO algorithms. The experimental results showed that the PSMRFO algorithm achieved higher charging coverage rate compared to the other algorithms.