A Dynamic Multiagent Genetic Algorithm for Optimal Charging in Wireless Rechargeable Sensor Networks

Yating Cao, Jing Liu, Zhouwu Xu

Abstract: Wireless energy charging is considered as a very promising technology for prolonging sensor lifetime in Wireless Rechargeable Sensor Networks (WRSNs). However, most existing studies suffers from charging scalability and efficiency problems. Under such a background, a new one to many charging model, which allows multiple sensors to be charged simultaneously by a single mobile reader, is designed. A dynamic multiagent genetic algorithm with a virtual force operator combined with K-means clustering for WRSNs (D_VF_MAGA_WRSNs) is proposed to optimize the charging problem in this paper. Agents representing candidate solutions compete or cooperate with their neighbors, and can also use knowledge. Memetic algorithm is applied in the process of evolution to guide readers moving to the tags' clustering centers obtained by the K-means algorithm. Moreover, a special crossover operator is designed to dynamically adjust the number of reader charging positions in the network. To verify the effectiveness of D_VF_MAGA_WRSNs, various of experiments on different kinds of benchmarks are carried. Compared with other algorithms, our algorithm is illustrated to be superior for WRSNs in terms of total charging time, maximum charging load, charging efficiency, and total charging distance.