Multi-objective Optimization of RFID Network Based on Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Pan:2011:ITJ,
  author =       "Weijie Pan and Shaobo Li and Qingsheng Xie and 
                 Guanci Yang",
  title =        "Multi-objective Optimization of {RFID} Network Based
                 on Genetic Programming",
  journal =      "Information Technology Journal",
  year =         "2011",
  volume =       "10",
  number =       "12",
  pages =        "2427--2433",
  publisher =    "Asian Network for Scientific Information",
  keywords =     "genetic algorithms, genetic programming, RFID network,
                 multi-objective optimisation, load balancing",
  ISSN =         "18125638",
  URL =          "http://docsdrive.com/pdfs/ansinet/itj/2011/2427-2433.pdf",
  DOI =          "doi:10.3923/itj.2011.2427.2433",
  size =         "7 pages",
  abstract =     "With the widespread application of RFID tags, the
                 layout of RFID readers under guaranteed the rate of
                 coverage, RFID network load balance and communication
                 quality which becomes a major focus of current research
                 on RFID network. Present study analyses the
                 characteristics of RFID network and the disadvantages
                 of current optimisation methods on readers network, by
                 establishing the mathematical optimisation model of
                 RFID network, a kind of method that multi-objective
                 optimisation of RFID network based on Genetic
                 Programming is proposed and the evolutional topological
                 operators, terminal set and fitness functions are
                 designed. Finally, it realised the module of the
                 multi-objective optimisation algorithm, the number of
                 readers and the layout of readers automatic
                 optimisation. The experimental results show that it has
                 higher efficiency, faster convergence rate and good
                 accuracy. It can keep well balance between topology and
                 parameter search. This research has important reference
                 value in the theory and practice.",
  bibsource =    "OAI-PMH server at www.doaj.org",
  language =     "eng",
  oai =          "oai:doaj-articles:e86f6d80e8dd3e505e0bac5d38f46adb",
}

Genetic Programming entries for Weijie Pan Shaobo Li Qingsheng Xie Guanci Yang

Citations