基于粒子群遗传算法的智能组卷策略

在线考试摒弃了传统纸质考试固有的缺点，在网络教育领域里获得了非常广泛的应用。人工智能化考试组卷，是完成在线考试高效性的重要技术之一。组卷问题，是多发展目标的组合优化问题，一般来说具备数个解。人工智能算法对于寻找此类问题的最优解具有明显优势。本文首先分析和研究目前主流的智能组卷算法，并结合组卷的有关原理及数学实验模型，提出一种基于粒子群遗传算法的智能组卷策略，将群体中的粒子和个体极值、群体极值进行遗传算法中的交叉操作与粒子本身展开变异操作，同时通过自适应调节交叉概率和变异概率、分段实数编码等方式，提升算法性能。最后经过对比实验验证该算法的优势。

Intelligent Test Paper Generation Strategy Based on Particle Swarm Optimization Genetic Algorithm

Online exams abandon the inherent shortcomings of traditional paper exams and have been widely used in the field of online education. The test paper of artificial intelligence is one of the important techniques for completing online examinations efficiently. The question of test paper is a multi-development goal combination and optimization problem, and generally has several solutions. Artificial intelligence algorithms have obvious advantages in finding the optimal solution of such problems. This paper first analyzes and studies the current mainstream intelligent test paper generation algorithm, combines the relevant principles of test paper generation and mathematical experiment models, and proposes an intelligent test paper generation strategy based on particle swarm genetic algorithm. The particles, individual extremes in the population and the extremes of the population are performed the crossover operation in the genetic algorithm and the mutation operation of the particle itself. At the same time, the algorithm performance is improved by adaptively adjusting the crossover probability and the mutation probability, and the segmented real number encoding. Finally, a comparative experiment is taken to prove the advantages of the algorithm.