• DocumentCode
    2261466
  • Title

    Fuzzy Optimization Method Based on Dynamic Uncertainty Restriction

  • Author

    Jin, Chenxia ; Li, Fachao

  • Author_Institution
    Sch. of Econ. & Manage., Hebei Univ. of Sci. & Technol., Shijiazhuang
  • Volume
    1
  • fYear
    2008
  • fDate
    20-22 Dec. 2008
  • Firstpage
    736
  • Lastpage
    740
  • Abstract
    Fuzzy optimization is a well-known optimization problem in artificial intelligence, manufacturing and management, establishing general and operable fuzzy optimization methods are important in both theory and application. In this paper, by analyzing the essential characteristic of uncertain optimization, based on the idea of dynamic uncertainty criteria, we establish a fuzzy optimization model based on dynamic uncertainty restriction; then we give a solution method based on principal operation and dynamic uncertainty restriction (denoted by BPUO-FGA, for short), by combining with genetic algorithm; finally, we analyze the performance of BPUO-FGA by Markov chain theory and an example.
  • Keywords
    Markov processes; fuzzy systems; genetic algorithms; uncertainty handling; BPUO-FGA; Markov chain theory; artificial intelligence; dynamic uncertainty restriction; fuzzy optimization method; genetic algorithm; Algorithm design and analysis; Artificial intelligence; Evolutionary computation; Fuzzy sets; Genetic algorithms; Information analysis; Optimization methods; Performance analysis; Technology management; Uncertainty; Fuzzy optimization; Markov chain; fuzzy genetic algorithm; principal operation; uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-0-7695-3497-8
  • Type

    conf

  • DOI
    10.1109/IITA.2008.257
  • Filename
    4739669