The Journal of China Universities of Posts and Telecommunications ›› 2021, Vol. 28 ›› Issue (6): 91-102.doi: 10.19682/j.cnki.1005-8885.2021.1012

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Hybrid gray wolf optimization-cuckoo search algorithm for RFID network planning


  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:2020-12-22 Revised:2021-07-28 Online:2021-12-30 Published:2021-12-30
  • Supported by:
     the National Natural Science Foundation of China ( 61761004 ), and the Natural Science Foundation of Guangxi Province, China ( 2019GXNSFAA245045)

Abstract: In recent years, with the rapid development of Internet of things (IoT) technology, radio frequency identification (RFID) technology as the core of IoT technology has been paid more and more attention, and RFID network planning(RNP) has become the primary concern. Compared with the traditional methods, meta-heuristic method is widely used in RNP. Aiming at the target requirements of RFID, such as fewer readers, covering more tags, reducing the interference between readers and saving costs, this paper proposes a hybrid gray wolf optimization-cuckoo search (GWO-CS) algorithm. This method uses the input representation based on random gray wolf search and evaluates the tag density and location to determine the combination performance of the reader's propagation area. Compared with particle swarm optimization ( PSO) algorithm, cuckoo search( CS) algorithm and gray wolf optimization ( GWO) algorithm under the same experimental conditions, the coverage of GWO-CS is 9.306% higher than that of PSO algorithm, 6.963% higher than that of CS algorithm, and 3.488% higher than that of GWO algorithm. The results show that the GWO-CS algorithm cannot only improve the global search range, but also improve the local search depth.

Key words: radio frequency identification (RFID), gray wolf optimization ( GWO) algorithm, cuckoo search ( CS) algorithm, dynamic adjustment of discovery probability, directional mutation

CLC Number: