基于遗传算法的学生在线竞赛系统研究

Research on student online competition system based on genetic algorithm

针对传统学生在线竞赛系统组卷效率低、系统性能差的问题,构建一个基于粒子群遗传算法的学生在线竞赛系统。基于此系统,在粒子群算法中融入遗传操作,提出一种基于粒子群遗传算法的智能组卷策略,改进交叉变异方式以加快算法收敛,减少组卷时间,提升在线竞赛系统的运行效率。实验结果表明,在智能组卷方面,本算法的组卷知识点覆盖率高达99.3%,比遗传算法和粒子群算法明显更高,更能满足用户需求。且在相同条件下,本算法的收敛速度更快、损耗时间更短,运行效率更高,系统整体性能显著提升,具有一定的可行性。

According to the problems of low group volume efficiency and poor system performance,a student online competition system based on particle swarm genetic algorithm is constructed.Based on this system,the genetic operation is integrated into the particle swarm algorithm,and an intelligent group volume strategy based on the particle swarm genetic algorithm is proposed to improve the cross variation mode to accelerate the algorithm convergence,reduce the group volume time,and improve the operation efficiency of the online competition system.The experimental results show that in terms of intelligent group volume,the coverage of this algorithm is 99.3%,which is significantly higher than the genetic algorithm and particle group algorithm and can meet the needs of the user.Under the same conditions,the convergence speed of this algorithm is faster,the loss time is shorter,the operation efficiency is higher,and the overall performance of the system is significantly improved,with certain feasibility.