Computer Science ›› 2021, Vol. 48 ›› Issue (1): 253-257.doi: 10.11896/jsjkx.200200095

Special Issue: Internet of Things

• Artificial Intelligence • Previous Articles     Next Articles

Improved Grey Wolf Optimizer for RFID Network Planning

QUAN Yi-xuan, ZHENG Jia-li, LUO Wen-cong, LIN Zi-han, XIE Xiao-de   

  1. School of Computer,Electronics and Information,Guangxi University,Nanning 530004,China
    Guangxi Key Laboratory of Multimedia Communications and Network Technology,Nanning 530004,China
  • Received:2020-02-21 Revised:2020-06-23 Online:2021-01-15 Published:2021-01-15
  • About author:QUAN Yi-xuan,born in 1996,postgraduate.Her main research interests include RFID and so on.
    ZHENG Jia-li,born in 1979,professor.His main research interests include internet of things,RFID and artificial intelligence.
  • Supported by:
    National Natural Science Foundation of China(61761004) and Natural Science Foundation of Guangxi Province,China(2019GXNSFAA245045).

Abstract: With the rapid development of Internet of things technology,radio frequency identification(RFID) system,with its advantages of non-contact and rapid identification,has become the first choice to solve the problem of Internet of things.RFID network planning should consider multiple objectives,which has been proved to be a multi-objective optimization problem.In this paper,an improved grey wolf optimizer is proposed,which uses Gauss mutation operator and inertia constant strategy to realize RFID network planning.Through the establishment of the optimization model,on the basis of satisfying the four objectives of 100% coverage of tags,deploying fewer readers,avoiding signal interference and consuming less power,this paper makes a comparative analysis with particle swarm optimization(PSO),genetic algorithm(GA) and monarch butterfly algorithm(MMBO).The experimental results show that grey wolf algorithm performs better in RFID network planning.In the same experimental environment,compared with other algorithms,the fitness value of IGWO is 20.2% higher than GA,13.5% higher than PSO,9.66% higher than MMBO,and the number of tags covered is more,so the optimization scheme can be found more effectively.

Key words: Gauss mutation, Grey wolf algorithm, Inertia constant, Network planning, RFID

CLC Number: 

  • TP301.6
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