Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (9): 292-297.doi: 10.11896/jsjkx.200700167

• Computer Network • Previous Articles     Next Articles

Optimized Deployment of RFID Reader Antenna Based on Improved Multi-objective Salp Swarm Algorithm

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

  1. School of Computer,Electrionics and Information,Guangxi University,Nanning 530004,ChinaGuangxi Key Laboratory of Multimedia Communications and Network Technology,Nanning 530004,China
  • Received:2020-07-26 Revised:2020-09-25 Online:2021-09-15 Published:2021-09-10
  • About author:LUO Wen-cong,born in 1996,postgra-duate.His main research interests include RFID and swarm intelligence.
    ZHENG Jia-li,born in 1979,Ph.D,professor,is a member of China Computer Federation.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)

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Abstract: ith the rapid development of radio frequency identification (RFID) technology,in a variety of special environments (such as factories,warehouses,prisons,etc.),the demand for optimal deployment of RFID reader antennas has attracted extensive attention.In order to solve the problems in the deployment of RFID reader antenna,such as difficult deployment,many constraints and difficult to find the optimal solution and Pareto front,this paper proposes an optimized deployment method of RFID reader antenna based on the improved multi-objective SALP swarm algorithm (MSSA).The multi-objective optimization deployment model of RFID reader antenna is constructed in advance,and the optimization target is set.The multi-objective tympana algorithm is used to train the optimal deployment model of RFID reader antenna.The separation operator is introduced to optimize the search ability,and the non dominated solutions satisfying the conditions are searched continuously through iteration,and the Pareto solution set satisfying the conditions is constructed,which is the optimization result.The results show that the proposed algorithm has faster convergence rate than the algorithms of BA-OM,PSO and MC-BFO without the prior knowledge,coverage rate increases by 33%,28% and 20% respectively.Compared with the same type of hybrid firefly (HMOFA) algorithm for Pareto solution set,the load balancing is increased by 7.14%,the economic benefit is increased by 59.74%,and the reader interfe-rence is reduced by 34.04%.

Key words: Multi-objective salp swarm algorithm, Optimal deployment, Pareto set, RFID, Seperating operator

CLC Number: 

  • TP301.6
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