Improved sparrow search algorithm for RFID network planning

To solve the problem that the performance of the coverage, interference rate, load balance andweak power in theradio frequency identification (RFID) network planning. This paper proposes an elite opposition-based learningand Levy flight sparrow search algorithm (SSA), which is named elite opposition-based learning and Levy flightSSA (ELSSA). First, the algorithm initializes the population by an elite opposed-based learning strategy toenhance the diversity of the population. Second, Levy flight is introduced into the scrounger's position updateformula to solve the situation that the algorithm falls into the local optimal solution. It has a probability that thecurrent position is changed by Levy flight. This method can jump out of the local optimal solution. In the end, theproposed 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 ofreaders, the average fitness of the ELSSA is improved respectively by 3.36%, 5.67% and 18.45%. By settingthe different number of readers, ELSSA uses fewer readers than other algorithms. The conclusion shows that theproposed method can ensure a satisfying coverage by using fewer readers and achieving higher comprehensiveperformance.