改进量子行为粒子群算法智能组卷策略研究

Study on Intelligent Test Paper Generation Strategy through Improved Quantum-behaved Particle Swarm Optimization

提出一种求解智能组卷问题的改进量子粒子群算法。首先,通过定义粒子进化速度及粒子聚集度,将惯性权重表示为粒子进化速度和粒子聚集度的函数,使惯性权重具有自适应性。其次,将慢变函数引入传统位置更新公式中,以有效地解决陷入局部最优解的问题。最后,根据项目反应原理对组卷问题进行数学建模。仿真实验表明,与标准粒子群算法和量子粒子群算法相比,所提算法在组卷成功率和组卷效率方面均具有更好的性能。

One improved Quantum-behaved particle swarm optimization for intelligent test paper generation was put forward.First of all,inertia weight is expressed as functions of particle evolution velocity and particle aggregation by defining them.Secondly,slowly varying function is introduced into the traditional position updating formula to effectively overcome the problem of getting into the local optimal solution.Finally,mathematical modeling is set for test paper genera-tion problems based on item response theory.Simulation results show that,comparing with the standard particle swarm optimization algorithm and quantum particle swarm algorithm,the proposed algorithm is of better performance in the success rate and efficiency of test paper generation.