Self-optimization examination system based on improved particle swarm optimization

Abstract Artificial intelligence has been applied to many fields successfully and saved many human and material resources. The intelligent examination system is a typical application case, which makes teachers can not only master the study situation of every candidate at any time but also design further study plans with the help of the examination system. A self-optimization examination system is shown in this paper, which is carried out by an improved particle swarm optimization. The intelligent examination system can surmount two difficulties shown in the construction of the traditional examining system, one is the setting of the attributes of the examination questions, and another is the maintenance of the database of the examination questions. The experiment shows that the novel method can not only optimize the attributes of the questions in the examination database intelligently but also maintain the examination database effectively through massive training.