[1]程美英,钱乾.二元群智能算法求解组卷问题研究[J].计算机技术与发展,2013,(05):79-82.
　CHENG Mei-ying,QIAN Qian.Research on Binary Swarm Intelligence Algorithm for Test Paper Problem[J].,2013,(05):79-82.
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二元群智能算法求解组卷问题研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2013年05期
页码:
79-82
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
Research on Binary Swarm Intelligence Algorithm for Test Paper Problem
文章编号:
1673-629X（2013）05-0079-04
作者:
程美英1; 钱乾1; 2
[1]安徽商贸职业技术学院 电子信息工程系;[2]安徽工程大学 计算机与信息学院
Author(s):
CHENG Mei-ying; QIAN Qian
关键词:
二元蚁群算法; 二元粒子群算法; 组卷问题; 时间性能对比分析
Keywords:
binary ant colony algotithm; binary particle swarm optimization; test paper problem; time performance analysis
文献标志码:
A
摘要:
二元蚁群优化算法(BACO-CA)及二元粒子群优化算法(BPSO-CA)作为基于概率的随机搜索智能算法,二者在寻优机理上有着显著的不同.以大规模组合优化问题组卷问题为例,通过设置算法中的参数,探讨二元蚁群优化算法和二元粒子群优化算法求解组卷问题性能的优劣.仿真实验表明,二元蚁群优化算法和二元粒子群优化算法虽然均能在多项式时间内完成组卷问题的求解,但二元粒子群优化算法在求解组卷问题时较二元蚁群优化算法具有更好的时间性能,能在较短的时间收敛到全局最优解
Abstract:
As a random search algorithm based on probability,the binary ant colony algorithm ( BACO-CA) and the binary particle swarm optimization (BPSO-CA) has the different optimization mechanism. Take the test paper for example,by setting the parameters of the algorithm,the performance of the BACO-CA and the BPSO-CA for the test paper problem was discussed. Experimental results show that the two algorithm can finish the group problem solving in polynomial time,but the BPSO-CA has the good performance,and can solve the test paper problem in polynomial time,and converge to the global optimal solution in a short time
更新日期/Last Update: 1900-01-01