基于蚁群算法思想的智能考试系统模型研究

Intelligent test system model based on ant colony optimization

针对传统考试耗时耗力等缺点,提出基于解离散优化问题蚁群算法思想的智能考试系统模型。该模型从智能考试系统的需求出发对蚁群算法的信息素初始值的设定进行了探讨并改进了更新规则,将考试结果反馈给系统,从而不仅有效解决了自动组卷问题,而且系统具有了自主学习能力,使其能够更智能化地改进系统性能。经检验,该系统具有组卷速度快且选取试题重复率低等优点,算法有效可行,借助该系统组织的考试能够达到预期目标。

The paper proposed a model of intelligent test system since the traditional test was time-consuming, labor-intensive and unstable. Based on the demand of the intelligence test system, the model explored the setting of the initial value informa- tion of the ant colony optimization and updated the rules so that the test results could be feed backed to the system. This model not only effectively solved the problem of the auto-generating test-paper, but also improved the autonomous learning ability of the system, which was more intelligent to improve the performance. The practical test proves that the system has achieved the expected goal with high quality and high efficiency. The practical test shows that the system has advantages like fast generation of test papers, low repetition of the selected papers and effective colony algorithm. Examinations designed based on this system can achieve the expected testing goals.