• DocumentCode
    584336
  • Title

    The Application of Optimized Particle Swarm Algorithm in Non-paper Examination

  • Author

    Liang, Zhou ; Lixin, Ke ; Wu, Kaijun ; Jianmin, Gong ; Jian, Hua

  • Author_Institution
    Modern Inf. & Educ. Tech. center, Shanghai Ocean Univ., Shanghai, China
  • fYear
    2012
  • fDate
    11-13 Aug. 2012
  • Firstpage
    502
  • Lastpage
    506
  • Abstract
    To deal with non-paper test composition algorithm impact on exam quality, we proposed the test-sheet composition algorithms. By comparing a variety of existing intelligent algorithms in the application of test-sheet composition, we identify the shortcomings of existing algorithms, such as the "premature" of algorithm due to the poor local search ability and the low convergence rate, etc. PSO algorithm has no crossover, mutation operators. It directly provides the speed, position update formula, and completes the assessment with the help of the fitness function of iterations. The principles and mechanisms of algorithm are simpler. On the basis of standard PSO algorithm, we proposed a Binary Particle Swarm Optimize (BPSO) algorithm based on probability. Bayes formula was used to overcome the human factors impacting on algorithm convergence speed. The algorithm validity has been shown in the simulation experiment with Java.
  • Keywords
    Bayes methods; computer aided instruction; convergence; particle swarm optimisation; search problems; BPSO algorithm; Bayes formula; Java; algorithm convergence speed; algorithm validity; assessment completion; binary particle swarm optimize algorithm; convergence rate; crossover operator; exam quality; human factor; intelligent algorithm; iteration fitness function; local search ability; mutation operator; nonpaper examination; nonpaper test composition algorithm; optimized particle swarm algorithm; position update formula; probability; test-sheet composition algorithm; Algorithm design and analysis; Convergence; Genetic algorithms; Mathematical model; Particle swarm optimization; Sociology; Statistics; Bayes formula; binary; non-paper examination;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science & Service System (CSSS), 2012 International Conference on
  • Conference_Location
    Nanjing
  • Print_ISBN
    978-1-4673-0721-5
  • Type

    conf

  • DOI
    10.1109/CSSS.2012.132
  • Filename
    6394370