一种求解组卷问题的量子粒子群算法

摘要

为提高智能组卷的效率,提出一种求解组卷问题的带自适应变异的量子粒子群优化(AMQPSO)算法.首先在算法中嵌入有效判断早熟停滞的方法,一旦检索到早熟迹象,根据构造的变异概率对粒子进行变异使粒子跳出局部最优；其次基于项目反应理论,构建分步组卷问题的数学模型,减少组卷冗余度和提高组卷效率.仿真实验表明,与遗传算法相比,所提出的算法在组卷成功率和组卷质量方面均具有更好的性能.%This paper puts forward an adaptive mutation of the quantum particle swarm optimization (AMQPSO) algorithm in order to improve the efficiency of autogenerating test paper. Firstly, a method of effective premature and stagnation judgement is embedded in the algorithm. Once premature signs are retrieved, the algorithm mutates particles to jump out of the local optimum particle according to the structure mutation. Secondly, the algorithm constructs a mathematical model of autogenerating test paper in steps based on Item Response Theory to reduce redundancy and improve the efficiency of autogenerating. Simulation results showed that compared with the genetic algorithm, the proposed algorithm is of better performance in both success rate and quality of autogenerating test paper.