Abstract: 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 Levy 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, Levy 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 Levy 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.

Key words: radio frequency identification, network planning, sparrow search algorithm, elite opposition-based learning, Levy flight