A study on energy consumption of elevator group supervisory control systems using genetic network programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Yu:2009:ieeeSMC,
  author =       "Lu Yu and Shingo Mabu and Tiantian Zhang and 
                 Kotaro Hirasawa and Tsuyoshi Ueno",
  title =        "A study on energy consumption of elevator group
                 supervisory control systems using genetic network
                 programming",
  booktitle =    "IEEE International Conference on Systems, Man and
                 Cybernetics, SMC 2009",
  year =         "2009",
  month =        "11-14 " # oct,
  pages =        "583--588",
  abstract =     "Elevator group supervisory control system (EGSCS) is a
                 traffic system, where its controller manages the
                 elevator movement to transport passengers in buildings
                 efficiently. Recently, artificial intelligence (AI)
                 technology has been used in such complex systems.
                 Genetic network programming (GNP), a graph-based
                 evolutionary method extended from GA and GP, has been
                 already applied to EGSCS. On the other hand, since
                 energy consumption is becoming one of the greatest
                 challenges in the society, it should be taken as
                 criteria of the elevator operations. Moreover, the
                 elevator with maximum energy efficiency is therefore
                 required. Finally, the simulations show that the
                 elevator system has the higher energy consumption in
                 the light traffic, thus, some factors have been
                 introduced into GNP for energy saving in this paper.",
  keywords =     "genetic algorithms, genetic programming, genetic
                 network programming, AI technology, EGSCS, GA, GNP, GP,
                 artificial intelligence technology, building passenger
                 transport, complex system, elevator group supervisory
                 control system, energy consumption, energy saving,
                 graph-based evolutionary method, maximum energy
                 efficiency, traffic control system, graph theory,
                 intelligent control, large-scale systems, lifts",
  DOI =          "doi:10.1109/ICSMC.2009.5346621",
  ISSN =         "1062-922X",
  notes =        "Also known as \cite{5346621}",
}

Genetic Programming entries for Lu Yu Shingo Mabu Tiantian Zhang Kotaro Hirasawa Tsuyoshi Ueno

Citations