[1]吴琼.智能排课优化算法研究[J].延边大学学报(自然科学版),2015,41(04):331-336.
WU Qiong.Research of optimization algorithm for course timetabling problem[J].Journal of Yanbian University,2015,41(04):331-336.
点击复制
WU Qiong.Research of optimization algorithm for course timetabling problem[J].Journal of Yanbian University,2015,41(04):331-336.
智能排课优化算法研究
《延边大学学报(自然科学版)》[ISSN:1004-4353/CN:22-1191/N]
卷:
第41卷
期数:
2015年04期
页码:
331-336
栏目:
应用科学研究
出版日期:
2015-12-20
- Title:
- Research of optimization algorithm for course timetabling problem
- 文章编号:
- 1004-4353(2015)04-0331-06
- Keywords:
- university course timetabling; teaching effect praise degree; self-adaptive adjustment; hybrid genetic algorithm; hybrid genetic algorithm
- 分类号:
- TP391.9
- 文献标志码:
- A
- 摘要:
- 为解决高校排课优化问题,建立了以教学效果好评度最大化为优化目标的排课数学模型.针对传统遗传算法的不足,给出了一种混合遗传算法,该算法不仅能够对传统遗传算法的交叉率、变异率进行自适应改进,还能够实现冲突检测与消除功能.测试结果表明,该算法比传统的遗传算法、贪婪算法和蚁群算法耗时短,而且教学效果好评度最高,这说明该算法能有效缩短排课时间,提高排课质量和效率,实现高校排课智能化.
- Abstract:
- In order to solve the problem of university course timetabling, a mathematic model was established based on the objective function of maximum teaching effect praise degree. Aiming at the shortcoming of traditional genetic algorithm, a hybrid genetic algorithm which combined with self-adaptive crossover and mutation were put forward. The conflict detection and elimination function were realized. The test result show that hybrid algorithm was faster than traditional genetic algorithm, greedy algorithm and ant colony algorithm but the highest teaching effect praise degree. It proved that the algorithm shorten the time consuming, improve the quality and implementation the intelligence of course timetabling effectively.
参考文献/References:
[1] 孙丹莹.高校排课问题与算法分析[J].信息与电脑,2012,10:210-211.
[2] 钟耀广,刘群锋.基于遗传算法的高校排课数学模型[J].东莞理工学院学报,2012,10(19):4-8.
[3] 陈行平,陈江,陈启华.基于遗传算法的高校排课系统设计[J].绍兴文理学院学报,2004,34(20):25-28.
[4] 赵耀锋.基于贪婪算法的多媒体教室排课算法设计[J].信息技术,2013,13:80-82.
[5] 何小虎.一种改进蚁群算法在排课中的应用研究[J].电子设计工程,2012,8(20):28-29.
[6] 宗薇.高校智能排课系统算法的研究与实现[J].计算机仿真,2011,28(12):389-392.
[7] 范文广.基于遗传算法的排课设计[J].齐齐哈尔大学学报,2011,27(5):27-29.
[8] 刘化龙,胡钋,青志明.基于混合遗传算法的变压器局部放电超声定位法[J].兰州理工大学学报,2014,12(40):105-109.
[9] 刘秋红,寒枫,张钰,等.基于分层的自适应遗传算法在UTP中的应用研究[J].贵州大学学报(自然科学版),2007,24(2):184-187.
备注/Memo
作者简介: 吴琼(1979—),男,讲师,研究方向为网络技术与智能优化算法.
更新日期/Last Update:
2015-12-20