A Decomposition-based Multi-objective Self-adaptive Differential Evolution Algorithm for RFID Network Planning

In radio frequency identification (RFID) networks, designing the positions and the transmitter power parameters of readers to achieve optimal coverage, interference, load balance, power consumption, and total cost is the task of RFID network planning (RNP) problems, which is the core challenge and an NP-hard optimization problem, where nature-inspired optimization methods have been proved extremely useful. In this paper, on account of the well-known multi-objective evolutionary algorithm based on decomposition (MOEA/D), we propose a Decomposition-based Multi-objective Self-adaptive Differential Evolution algorithm (MOSDE/D), in which all these objective functions are optimized simultaneously in a single run by decomposition, and the improved cyclic crowding distance sorting strategy is introduced to ensure the diversity of solutions. Our approach is tested on standard static RFID networks and compared with other algorithms. Our approach is better than other compared methods in terms of the coverage, interference, load balance, number of readers, and power consumption.