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| 求解TSP问题的快速蚁群算法 | |
| 引用本文： | 申铉京,刘阳阳,黄永平,徐铁,何习文.求解TSP问题的快速蚁群算法[J].吉林大学学报(工学版),2013,43(1):147-151. |
| 作者姓名： | 申铉京 刘阳阳 黄永平 徐铁 何习文 |
| 作者单位： | 1. 吉林大学计算机科学与技术学院,长春130012;吉林大学符号计算与知识工程教育部重点实验室,长春130012 2. 吉林石油集团有限责任公司洮河农场,吉林松原,138000 3. 吉林大学计算机科学与技术学院,长春,130012 |
| 基金项目： | 吉林省自然科学基金项目(201115025);符号计算与知识工程教育部重点实验室开放基金项目(450060445325);吉林大学研究生创新基金项目(20111063);吉林大学“大学生创新性实验计划”项目(2011A53100) |
| 摘 要： | 针对蚁群算法求解旅行商问题时存在收敛速度慢并容易陷入局部最优的问题,提出了一种改进的蚁群算法。改进算法采用信息素挥发因子自适应调整机制,调节算法收敛速度,保证算法的全局搜索能力。同时根据公共路径降低蚁群算法运算时间,诱导蚁群寻找更优解。实验结果表明,改进算法在迭代次数相对较少的情况下求得的平均解与已知最优解偏差为0.46%,最优解与已知最优解偏差为0.23%,在收敛速度及求解精度上均取到了较好的效果。 |
| 关 键 词： | 人工智能 蚁群算法 公共路径 自适应 旅行商问题 |
Fast ant colony algorithm for solving traveling salesman problem |
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| SHEN Xuan-jing,LIU Yang-yang,HUANG Yong-ping,XU Tie,He Xi-wen.Fast ant colony algorithm for solving traveling salesman problem[J].Journal of Jilin University:Eng and Technol Ed,2013,43(1):147-151. | |
| Authors: | SHEN Xuan-jing LIU Yang-yang HUANG Yong-ping XU Tie He Xi-wen |
| Affiliation: | 1(1.College of Computer Science and Technology,Jilin University,Changchun 130012,China;2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University,Changchun 130012,China;3.Taohe Farm,Jilin Oil Refco Group Ltd,Songyuan 138000,China) |
| Abstract: | Using ant colony algorithm to solve Traveling Salesman Problem(TSP) has certain disadvantages,such plunging into local minimum,slower convergence speed and so on.In order to find the optimal path accurately and rapidly,an improved ant colony algorithm is proposed.The improved algorithm uses adaptive adjusting mechanism of pheromone decay parameter to adjust the convergence speed and ensure the global search ability.With the help of common path,the computation time is reduced and the better solution can be obtained.Experiments show that,in a relatively few iterations,the percentage deviation of the average solution to the best known solution is 0.46%,and the percentage deviation of the best solution to the best known solution is 0.23%.The improved algorithm has high accuracy and convergence speed. |
| Keywords: | artificial intelligence ant colony algorithm common path adaptive traveling salesman problem |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |
