Two-Level Master-Slave RFID Networks Planning via Hybrid Multiobjective Artificial Bee Colony Optimizer

Lianbo Ma, Xingwei Wang, Min Huang, Zhiwei Lin, Liwei Tian, Hanning Chen

Research output: Contribution to journalArticlepeer-review

72 Citations (Scopus)
159 Downloads (Pure)


Radio frequency identification (RFID) networks planning (RNP) is a challenging task on how to deploy RFID readers under certain constraints. Existing RNP models are usually derived from the flat and centralized-processing framework identified by vertical integration within a set of objectives which couple different types of control variables. This paper proposes a two-level RNP model based on the hierarchical decoupling principle to reduce computational complexity, in which the cost efficient planning at the top levels is modeled with a set of discrete control variables (i.e., switch states of readers), and the quality of service objectives at the bottom level are modeled with a set of continuous control variables (i.e., physical coordinate and radiate power). The model of the objectives at the two levels is essentially a multi objective problem. In order to optimize this model, this paper proposes a specific multi objective artificial bee colony optimizer called H-MOABC, which is based on performance indicators with reinforcement learning and orthogonal Latin squares approach. The proposed algorithm proves to be competitive in dealing with two-objective and three-objective optimization problems in comparison with state-of-the-art algorithms. In the experiments, H-MOABC is employed to solve the two scalable real-world RNP instances in the hierarchical decoupling manner. Computational results shows that the proposed H-MOABC is very effective and efficient in RFID networks optimization.
Original languageEnglish
Pages (from-to)861-880
Number of pages20
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue number5
Early online date26 Jul 2017
Publication statusPublished (in print/issue) - 15 Apr 2019


  • Multi-objective Algorithm
  • Multi-level RNP


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