基于线性递减系数粒子群优化算法的组卷实现

为了避免目前常用的组卷算法组卷时间长、程序结构复杂、收敛速度慢等缺陷,提出基于线性递减系数粒子群优化算法的组卷策略。通过调整惯性系数,使得步长较小,惯性权系数的变化幅度小,这种减小趋势较为缓慢的方法能够避免陷入局部最优。并对数学模型以及线性递减惯性权系数进行了理论设计,同时通过编程实现了该算法。测试结果表明加入线性递减系数后运算迭代次数明显减少,证明加入线性递减系数后的组卷策略收敛性好,能够高效准确地按照一定的预期条件进行组卷,符合预期要求。

Implementation of test paper generation based on particle swarm optimization algorithm with linear decreasing inertia weight

In order to avoid defects in the commonly used test paper generation algorithm,such as too much time taking, complicated program structure and low velocity of convergence,a test paper generation strategy of particle swarm optimization algorithm based on linear decreasing inertia weight is proposed. The step size becomes smaller and the inertia weight changes less by adjusting the inertia coefficient. The relatively slow decreasing trend method can avoid falling into local optimum. The theoretical design for mathematical model,linear decreasing inertia weight was carried out. The algorithm was realized by pro？gramming. Test results show that the addition of linear decreasing coefficient can greatly reduce the iteration times,can make the convergence characteristic better,and can efficiently and accurately generate test paper according to the expected conditions.