基于Tabu搜索的选题算法研究与应用

针对目前传统选题算法在大型题库中选题效率低、质量不高的情况,给出了一个多约束条件下的选题问题模型,提出基于Tabu搜索算法的选题算法。依据Tabu搜索算法的集中和扩散两个策略,避开局部最大值,以最短时间寻找最优解,从而满足组卷要求。实验结果表明,该算法能有效准确的满足从大型试题库选题,其收敛速度和选题质量都有显著提高。

Reseach and application of item selecting algorithm based on Tabu search

Previous investigation has shown that the conventional algorithms in selecting test items from a large item bank were inefficient and poor quality. According to the item selecting question model of multiple assessment criteria, a Tabu search-based item selecting algorithm is proposed. Based on its concentrated and diffused strategy, the approach can avoid local maximum while meeting demand of composing test sheets. Experimental results indicated that this algorithm is more effective and accurate in selecting items from large item banks, and its convergence rate and item selecting quality have a distinct improvement.