Intelligent test system model based on ant colony optimization
基于蚁群算法思想的智能考试系统模型研究

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 information 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.