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 Lévy 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,Lévy 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 Lévy 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.

随着物联网技术的飞速发展,射频识别(Radio Frequency Identification,RFID)系统因具有非接触、快速识别等优点而成为了解决物联网问题的首选方案.RFID网络规划问题要考虑多个目标,被证明是多目标优化的问题.群体智能(Swarm In-telligence,SI)算法在解决多目标优化问题方面得到了广泛的关注.文中提出了一种改进型灰狼算法(Improved Grey Wolf Op-timizer,IGWO),利用高斯变异算子和惯性常量策略来实现RFID网络规划.通过建立优化模型,在满足标签100％覆盖率、部署更少的阅读器、避免信号干扰、消耗更少的功率4个目标的基础上,将所提算法与粒子群算法(Particle Swarm Optimization,PSO)、遗传算法(Genetic Algorithm,GA)、帝王蝶算法(Monarch Butterfly Algorithm,MMBO)进行了对比分析.实验结果表明,灰狼算法在RFID网络规划时表现更优异,在相同的实验环境下,相较于其他算法,IGWO的适应度值比GA提高了20.2％,比PSO提高了13.5％,比MMBO提高了9.66％;并且覆盖的标签数更多,可以更有效地求出最优化方案.

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.