首页 | 官方网站   微博 | 高级检索  
     

用量子蚁群算法求解大规模旅行商问题
引用本文:李煜,马良.用量子蚁群算法求解大规模旅行商问题[J].上海理工大学学报,2012,34(4):355-358.
作者姓名:李煜  马良
作者单位:1. 上海理工大学管理学院,上海 200093;河南大学管理科学与工程研究所,开封 475004
2. 上海理工大学管理学院,上海,200093
基金项目:国家自然科学基金资助项目,河南省科技攻关重点资助项目
摘    要:针对旅行商问题(TSP),提出了一种新的混合量子优化算法——量子蚁群算法.量子蚁群算法采用量子比特的概率幅表示蚂蚁的当前位置,采用量子旋转门更新蚂蚁的位置,选取国际通用的TSP实例库中多个实例进行测试.仿真实验表明,该算法具有很好的精确度和鲁棒性,可使搜索空间加倍,比传统的蚁群算法具有更好的种群多样性.

关 键 词:量子优化  蚁群算法  量子蚁群算法  旅行商问题

Solving Large scale Traveling Salesman Problem by Quantum Ant Colony Algorithm
LI Yu and MA Liang.Solving Large scale Traveling Salesman Problem by Quantum Ant Colony Algorithm[J].Journal of University of Shanghai For Science and Technology,2012,34(4):355-358.
Authors:LI Yu and MA Liang
Affiliation:1 (1.Business School,University of Shanghai for Science and Technology,Shanghai 200093,China; 2.Research Institute of Management Science and Engineering,Henan University,Kaifeng 475004,China)
Abstract:Based on the combination of the quantum theory and ant colony optimization,a novel algorithm,the quantum ant colony algorithm,was proposed.Ants’s positions were represented by a group of quantum bits and the quantum rotation gates were designed to update the ants’ positions for enabling the ants’ movements.The classical TSP was successfully solved by using the quantum ant colony algorithm,taking series of typical instances as the examples.The computational results show the effectiveness and robustness of the algorithm in numerical simulations.The algorithm can find the satisfactory solutions with a small size of populations and minimal relative error.
Keywords:quantum optimization  ant colony algorithm  quantum ant colony algorithm  traveling salesman problem(TSP)
本文献已被 CNKI 万方数据 等数据库收录!
点击此处可从《上海理工大学学报》浏览原始摘要信息
点击此处可从《上海理工大学学报》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司    京ICP备09084417号-23

京公网安备 11010802026262号