摘要
考试是教学活动中一个重要的环节。根据试卷的总分值、答题时间、试题类型、试题难度、考查的知识点等约束,从试题库中抽取若干试题组合为一张试卷。为了得到一份优秀的试卷,要求试卷中的试题必须满足众多的约束,将全局粒子群算法与局部粒子群算法相混合,并且引入了遗传算法中的交叉与变异算子,很好地解决了收敛速度与局部最优的问题。将改进的粒子群算法应用于智能组卷中,经过仿真试验表明,该方法能够在满足多个约束的同时,获得比较优秀的试卷组合结果。
The examination is an important part of teaching activities. According to the papers of the total score, the answer time, the item type, item difficulty, and examine the knowledge constraints, a number of questions the combination of a paper drawn from the test database. In order to get a good paper, the questions papers must meet many constraints, this paper, the global particle swarm optimization with local particle swarm optimization, mixed, and the introduction of the crossover and mutation operators in genetic algorithms, a good solution to convergence speed and local optimization. Improved particle swarm algorithm applied to the intelligent test, the simulation results show that this method can meet the multiple constraints, access to the excellent papers combined results.
出处
《信息技术》
2012年第9期165-167,171,共4页
Information Technology
关键词
智能组卷
粒子群算法
全局
局部
交叉
变异
inteUigendy composing test paper
particle swarm optimization
global
local
crossover
mutation
