• DocumentCode
    3312139
  • Title

    Combined quantum particle swarm optimization algorithm for multi-objective nutritional diet decision making

  • Author

    Lv, Youbo

  • Author_Institution
    Sch. of Comput. & Inf. Eng., Harbin Univ. of Commerce, Harbin, China
  • fYear
    2009
  • fDate
    8-11 Aug. 2009
  • Firstpage
    279
  • Lastpage
    282
  • Abstract
    A assembled method based on quantum particle swarm optimization (QPSO) algorithm combined with Bayesian networks (BN) is proposed to solve complex multi-objective nutritional diet decision making problem. To realize nutritional diet decision optimization for patients, BN model for dealing with associative relationship between diseases and diets is set up to compute and update the edibility of every food in database. QPSO algorithm is selected as the core optimization algorithm to avoid being trapped in a local optimum. Actual experimental results show that such combined method is a feasible and effective approach for actual nutritional diet decision making problem.
  • Keywords
    belief networks; decision making; diseases; health care; medical computing; particle swarm optimisation; patient care; quantum computing; BN model; Bayesian network; QPSO algorithm; assembled method; disease prevention; healthy nutritious food edibility; multiobjective patient nutritional diet decision making; quantum particle swarm optimization algorithm; Assembly; Bayesian methods; Business; Computer networks; Costs; Decision making; Diseases; Electronic mail; Particle swarm optimization; Quantum computing; Decision Making; Multi-Objective Optimization; Nutritional Diet; Quantum Particle Swarm Optimization Algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Information Technology, 2009. ICCSIT 2009. 2nd IEEE International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-4519-6
  • Electronic_ISBN
    978-1-4244-4520-2
  • Type

    conf

  • DOI
    10.1109/ICCSIT.2009.5234580
  • Filename
    5234580