A hybrid particle swarm optimization algorithm for RFID network planning

Abstract

The radio frequency identification (RFID) technology is widely used for object identification and tracking applications, which brings the most challenging RFID network planning (RNP) problem. However, existing RNP methods have some defects, such as the number of readers is uncertain and objectives conflict each other. In this paper, we propose a hybrid particle swarm optimization algorithm with K-means clustering and virtual forces for RNP, which is named as HPSO-RNP. HPSO-RNP can search the number of readers automatically and initialize the coordinates of readers through the K-means algorithms. Virtual force is integrated into the random movement to adjust the location of readers during the search process of PSO. Moreover, we consider four objective functions in a hierarchical manner. To compare HPSO-RNP with the existing method, extensive experiments are conducted on eight RNP benchmark datasets and the results validate that the performance of the proposed method is superior for planning RFID networks in terms of the number of readers, interference, power and load balance.

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Correspondence to Jing Liu.

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Cao, Y., Liu, J. & Xu, Z. A hybrid particle swarm optimization algorithm for RFID network planning. Soft Comput (2021). https://doi.org/10.1007/s00500-020-05569-1

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Keywords

  • Particle swarm optimization
  • Radio frequency identification
  • RFID network planning
  • K-means algorithm
  • Virtual force