基于改进粒子群算法的组卷系统

Test System based on Improved Particle Swarm Optimization

笔者首先在参阅国内外相关文献的基础上简单介绍了应用于组卷的常用的智能算法,并且对这些常用的组卷算法的优缺点进行了简单的分析。然后,对组卷系统进行了系统的研究,详细分析了试题和试卷的评价指标以及各项指标之间的关系和作用,在此基础上总结出试题库组卷的数学模型。粒子群算法因为涉及的变量较少、编码方便等一系列优点,受到许多学者的关注,很多学者改进了粒子群算法,因此,粒子群算法的应用领域越来越广,本系统结合组卷实际需求,改进离散粒子群算法,引入蚁群算法中的信息素概念,设计了基于改进粒子群算法的试题库系统,测试表明该算法对组卷问题是有效的。

The author first introduces the commonly used intelligent algorithms applied to the test paper on the basis of the relevant literature at home and abroad,and analyzes the advantages and disadvantages of these commonly used test methods.Then,the paper systematically studies the test paper system,analyzes the evaluation index of the questions and papers,and the relationship between the indexes and the indexes.On this basis,the mathematical model of the test paper is summarized.Particle swarm algorithm has been improved by many scholars because of the small number of variables involved and the convenience of coding,and many scholars have improved the particle swarm optimization algorithm.Therefore,the application of particle swarm optimization is becoming more and more extensive.The paper tries to improve the particle swarm optimization algorithm,and introduces the concept of pheromone in the ant colony algorithm.The test database system based on the improved particle swarm optimization algorithm is designed.The test results show that the algorithm is effective for the problem.