[1]易正俊[],李勇霞[],易校石[].自适应蚁群算法求解最短路径和TSP问题[J].计算机技术与发展,2016,26(12):1-5.
　YI Zheng-jun[],LI Yong-xia[],YI Xiao-shi[]. Solving of Shortest Path Problem and TSP with Adaptive Ant Colony Algorithm[J].,2016,26(12):1-5.
点击复制

自适应蚁群算法求解最短路径和TSP问题()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年12期
页码:
1-5
栏目:
智能、算法、系统工程
出版日期:
2016-12-10

文章信息/Info

Title:
 Solving of Shortest Path Problem and TSP with Adaptive Ant Colony Algorithm
文章编号:
1673-629X（2016)12-0001-05
作者:
 易正俊[1]; 李勇霞[1]; 易校石[2]
1.重庆大学 数学与统计学院,重庆；2.重庆师范大学 数学科学学院
Author(s):
 YI Zheng-jun[1]; LI Yong-xia[1]; YI Xiao-shi[2]
关键词:
 ; 蚁群算法; 最短路径; 方向引导; 动态因子; 旅行商问题
Keywords:
 ; ant colony algorithm; shortest path; direction guiding; dynamic factor; TSP
分类号:
TP301
文献标志码:
A
摘要:
 对传统蚁群算法的初始化信息素浓度加入方向引导，避免蚁群在初始阶段盲目地随机搜索浪费较多的时间；在全局信息素更新过程中加入双曲正切函数作为动态因子，自适应地更新每次迭代较优解路径的信息素浓度，增大算法获取全局最优解的可能性。两个算例采用改进的蚁群算法进行优化，优化的结果与实际情形具有良好的一致性，说明了改进算法的有效性和实用性。
Abstract:
 Direction guiding is utilized in the initial pheromone avoiding ant colony in the initial stage to blindly random search and to waste more time. Moreover,a dynamic factor ( hyperbolic tangent function) is invited in the global renewal process to update adaptively the pheromone concentration on the optimal path,in which way the possibility of obtaining the global optimal solution is increased. Then two examples are optimized with the improved algorithm,and the optimization results are in step with the actual,illustrating the effective-ness and practicability of the improved algorithm.

相似文献/References:

[1]段军,张清磊.蚁群算法在LEACH路由协议中的应用[J].计算机技术与发展,2014,24(01):65.
　DUAN Jun,ZHANG Qing-lei.Application of Ant Colony Algorithm Based on LEACH Routing Protocol[J].,2014,24(12):65.
[2]何小娜 逄焕利.基于二维直方图和改进蚁群聚类的图像分割[J].计算机技术与发展,2010,(03):128.
　HE Xiao-na,PANG Huan-li.Image Segmentation Based on Improved Ant Colony Clustering and Two- Dimensional Histogram[J].,2010,(12):128.
[3]熊伟平 曾碧卿.几种仿生优化算法的比较研究[J].计算机技术与发展,2010,(03):9.
　XIONG Wei-ping,ZENG Bi-qing.Studies on Some Bionic Optimization Algorithms[J].,2010,(12):9.
[4]宋世杰 刘高峰 周忠友 卢小亮.基于改进蚁群算法求解最短路径和TSP问题[J].计算机技术与发展,2010,(04):144.
　SONG Shi-jie,LIU Gao-feng,ZHOU Zhong-you,et al.An Improved Ant Colony Algorithm Solving the Shortest Path and TSP Problem[J].,2010,(12):144.
[5]林本强 唐依珠.基于蚁群算法的移动自适应网QoS路由算法[J].计算机技术与发展,2009,(06):9.
　LIN Ben-qiang,TANG Yi-zhu.Ant Colony Algorithm Based Ad Hoc Network QoS Routing Algorithm[J].,2009,(12):9.
[6]古明家 宣士斌 廉侃超 李永胜.基于蚁群和人工鱼群算法融合的QoS路由算法[J].计算机技术与发展,2009,(07):145.
　GU Ming-jia,XUAN Shi-bin,LIAN Kan-chao,et al.QoS Routing Algorithm Based on Combination of Modified Ant Colony Algorithm and Artificial Fish Swarm Algorithm[J].,2009,(12):145.
[7]贾瑞玉 张新建 冯伦阔 李永顺.信息素增量动态更新的改进蚁群算法[J].计算机技术与发展,2009,(09):32.
　JIA Rui-yu,ZHANG Xin-jian,FENG Lun-kuo,et al.Ant Colony Algorithm with Dynamic Pheromones Increment Updating[J].,2009,(12):32.
[8]鲍娜 张德贤 孙傲冰 王飞.基于改进蚁群算法的网格组合拍卖资源分配[J].计算机技术与发展,2009,(10):149.
　BAO Na,ZHANG De-xian,SUN Ao-bing,et al.Research on Resource Allocation of Combinatorial Auction in Grid Based on Improved Ant Colony Algorithm[J].,2009,(12):149.
[9]邓义乔 张代远.蚁群算法在搜索引擎系统中的应用研究[J].计算机技术与发展,2009,(12):21.
　DENG Yi-qiao,ZHANG Dai-yuan.Research and Application of Ant Colony Algorithm in Searching Engine System[J].,2009,(12):21.
[10]段凤玲 李龙澍 曹文婷.具有多态特征和聚类处理的蚁群算法[J].计算机技术与发展,2009,(12):77.
　DUAN Feng-ling,LI Long-shu,CAO Wen-ting.Ant Colony Algorithm with Polymorphism and Clustering Processing[J].,2009,(12):77.
[11]汪昡紫,孙宪坤,刘锴. 一种图像边缘检测算法的改进和实现[J].计算机技术与发展,2014,24(09):108.
　WANG Xuan-zi,SUN Xian-kun,LIU Kai. Improvement and Implementation for an Image Edge Detection Algorithm[J].,2014,24(12):108.
[12]陈世欢,李毅. 基于改进蚁群算法的改航路径规划[J].计算机技术与发展,2015,25(02):52.
　CHEN Shi-huan,LI Yi. Rerouting Planning Based on Improved Ant Colony Algorithm[J].,2015,25(12):52.
[13]邵明来,秦亮曦. 集粒度计算、蚁群算法与模糊思想的聚类算法[J].计算机技术与发展,2015,25(02):78.
　SHAO Ming-lai,QIN Liang-xi. Clustering Algorithm Combined Granular Computing,Ant Colony Algorithm and Fuzzy Idea[J].,2015,25(12):78.
[14]汪昡紫,孙宪坤,高飞. 轨道表面图像处理算法研究[J].计算机技术与发展,2015,25(09):182.
　WANG Xuan-zi,SUN Xian-kun,GAO Fei. Research on Algorithm of Track Surface Image Processing[J].,2015,25(12):182.
[15]谢骊玲,宋彦斌,杨坦,等. 求解车辆路径问题的改进MMAS算法[J].计算机技术与发展,2016,26(03):27.
　XIE Li-ling,SONG Yan-bin,YANG Tan,et al. An Improved MMAS for Vehicle Routing Problem[J].,2016,26(12):27.
[16]王春红[],任姚鹏[],姚喜妍[]. 一种改进的动态信任信息聚合算法[J].计算机技术与发展,2016,26(03):117.
　WANG Chun-hong[],REN Yao-peng[],YAO Xi-yan[]. An Improved Aggregation Algorithm of Dynamic Trust Information[J].,2016,26(12):117.
[17]张淑雯,刘效武,孙雪岩. 基于多源融合的网络安全态势层次感知[J].计算机技术与发展,2016,26(10):77.
　ZHANG Shu-wen,LIU Xiao-wu,SUN Xue-yan. Hierarchical Awareness of Network Security Situation Based on Multi-source Fusion [J].,2016,26(12):77.
[18]王伟杰,喻瑛,孙晓辉,等. 智能家居用电优化调度建模及蚁群算法求解[J].计算机技术与发展,2017,27(02):195.
　WANG Wei-jie,YU Ying,SUN Xiao-hui,et al. Modeling of Household Energy Consumption Scheduling and Its Solving with Ant Colony Algorithm[J].,2017,27(12):195.
[19]秦军[],董倩倩[],郝天曙[]. 基于蚁群模拟退火的云任务调度算法改进[J].计算机技术与发展,2017,27(03):117.
　QIN Jun[],DONG Qian-qian[],HAO Tian-shu[]. Improvement of Algorithm for Cloud Task Scheduling Based on Ant Colony Optimization and Simulated Annealing[J].,2017,27(12):117.
[20]刘梦青,王少辉. 基于蚁群算法的Storm集群资源感知任务调度[J].计算机技术与发展,2017,27(09):92.
　LIU Meng-qing,WANG Shao-hui. Research on Storm Resource-aware Task Scheduling with Ant Colony Algorithm[J].,2017,27(12):92.

更新日期/Last Update: 2017-02-03