Optimization of Radio-frequency Identification (RFID) Multi-tag Topology Based on Laser Ranging and Mind Evolutionary Algorithm (MEA)

In the application field of ultra high frequency (UHF) radio-frequency identification (RFID) multi-tag batch reading, the multi-tag topology has a significant impact on reading performance. To search the optimal topology corresponding to the maximum reading distance, this paper proposes a novel system which consists of two parts. The first is the dynamic reading of the distance testing system based on laser ranging and the other is charge-coupled device (CCD) data acquisition and analysis of multi-tag network topology based on the mind evolution algorithm (MEA). The forecasted reading range (FRR) performance is then evaluated by the actual reading range (ARR) measured via the proposed system to get the optimal topology. Finally, the MEA is compared with the previous method by error evaluation criteria and run time. The experiment shows that the mean absolute percentage error (MAPE) of the MEA reaches 2.01% and the average operating time is about 2.77 seconds. The results indicate that the proposed system can quickly and accurately extract the two-dimensional (2-D) coordinates of the RFID and the corresponding reading distance. MEA improves the calculation speed, predicts the maximum reading range, and optimizes the topology. The research is of great significance for the simultaneous identification of UHF RFID multi-tag.