The Journal of China Universities of Posts and Telecommunications ›› 2023, Vol. 30 ›› Issue (1): 93-102.doi: 10.19682/j.cnki.1005-8885.2023.2010

• Wireless • Previous Articles    

Improved sparrow search algorithm for RFID network planning

Zhang Jiangbo, Zheng Jiali, Quan Yixuan, Lin Zihan, Xie Xiaode   

  1. 1. School of Computer, Electrics and Information, Guangxi University, Nanning 530004, China
    2. Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning 530004, China
  • Received:2021-08-10 Revised:2022-03-19 Accepted:2023-02-13 Online:2023-02-28 Published:2023-02-28
  • Contact: Zheng Jiali, E-mail:
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61761004).

Abstract: To solve the problem that the performance of the coverage, interference rate, load balance andweak power in the radio frequency identification (RFID) network planning. This paper proposes an elite opposition-based learning and Levy flight sparrow search algorithm (SSA), which is named elite opposition-based learning and Levy flight SSA (ELSSA). First, the algorithm initializes the population by an elite opposed-based learning strategy to enhance the diversity of the population. Second, Levy flight is introduced into the scrounger's position update formula to solve the situation that the algorithm falls into the local optimal solution. It has a probability that the current position is changed by Levy flight. This method can jump out of the local optimal solution. In the end, the proposed method is compared with particle swarm optimization (PSO) algorithm, grey wolf optimzer (GWO) algorithm and SSA in the multiple simulation tests. The simulated results showed that, under the same number of readers, the average fitness of the ELSSA is improved respectively by 3.36%, 5.67% and 18.45%. By setting the different number of readers, ELSSA uses fewer readers than other algorithms. The conclusion shows that the proposed method can ensure a satisfying coverage by using fewer readers and achieving higher comprehensive performance.

Key words: radio frequency identification, network planning, sparrow search algorithm, elite opposition-based learning, Levy flight

CLC Number: