基于蚁群优化遗传算法的智能自动组卷算法研究

Research on intelligent automatic test paper generating algorithm based on ant colony optimizing genetic algorithm

针对基于遗传算法的计算机智能自动化组卷问题,提出一种能够提高求解效率的蚁群遗传融合优化算法。首先通过基于信息素的蚁群算法产生一个最优解并将其作为遗传算法的初始种群,从而有利于提高收敛性能。然后通过充分利用随机数并增加循环次数对传统遗传算法中基于轮盘赌的选择算法进行改进,从而确保下一代种群的多样性并提高最佳染色体被选择的机会。算法测试结果表明,相比传统的遗传算法,提出的蚁群优化遗传算法能够自动生成满足要求的试卷,并具有较高的组卷效率和质量。

An ant colony and genetic fusion optimization algorithm is proposed to improve the solution efficiency of the computer-aided automat test paper generation based on genetic algorithm.A pheromone-based ant colony algorithm is used to generate an optimal solution,and then the optimal solution is taken as the initial population of the genetic algorithm to improve the convergence performance.The selection algorithm based on roulette in the traditional genetic algorithm is improved by making full use of random number and increasing the cycle index,so as to ensure the diversity of the next generation of population and improve the selected opportunity of the best chromosome.The test results show that,in comparison with the traditional genetic algorithm,the proposed ant colony optimizing genetic algorithm can automatically generate the test papers satisfying the requirements,and has higher efficiency and quality of the test paper generation.