Mobile Charger Planning for Wireless Rechargeable Sensor Network Based on Ant Colony Optimization

Abstract

In order to provide a more flexible wireless rechargeable sensor network, a charger and a self-propelled vehicle are integrated into one vehicle in recent years. The path selection problem of mobile chargers can be formulated as the well-known travelling salesman problem. Therefore, metaheuristic algorithms can be applied to solve the planning problem of mobile chargers. Some researches presented planning methods based on the Simulated Annealing (SA) and Tabu Search (TS) algorithms but the results are not satisfied. In this paper, we not only design a novel encoding approach but also the fitness function for proposing an efficient planning algorithm based on the Ant Colony Optimization (ACO) algorithm. Simulation results show that the proposed ACO-based algorithm achieves a shorter planning path for a longer network lifetime compared with that generated by the SA and TS algorithms.