[1]张然 贾瑞玉 钱光超 李龙澍.带佳点交叉算子的非均匀窗口蚁群算法[J].计算机技术与发展,2007,(12):68-70.
　ZHANG Ran,JIA Rui-yu,QIAN Guang-chao,et al.Ant Colony Algorithm with Good- Point Crossover Operator Based on Different Size Window[J].,2007,(12):68-70.
点击复制

带佳点交叉算子的非均匀窗口蚁群算法()
分享到：

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

卷:
期数:
2007年12期
页码:
68-70
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
Ant Colony Algorithm with Good- Point Crossover Operator Based on Different Size Window
文章编号:
1673-629X（2007）12-0068-03
作者:
张然1; 2 贾瑞玉1 钱光超1 李龙澍1
[1]安徽大学计算机科学与技术学院[2]铜陵学院计算机科学与技术系
Author(s):
ZHANG Ran;  JIA Rui-yu;  QIAN Guang-chao;  LI Long-shu
[1]School of Computer Science and Technology, Anhui University[2]Dept. of Computer Science and Technology, Tongling College
关键词:
蚁群算法; 佳点集; 交叉算子; 窗口
Keywords:
ant colony algorithm： good- point set;  crossover operator;  window
分类号:
TP301.6
文献标志码:
A
摘要:
基本蚁群算法具有较强的鲁棒性，但收敛慢并容易陷入局部最优。针对这些缺陷，通过将蚂蚁的搜索空间缩减在非均匀的小窗口中，减少了蚂蚁的搜索时间。并将佳点集遗传算子引入到解的优化中来，提出了带佳点杂交算子的非均匀窗口蚁群算法，从本质上探索蚁群算法的寻优能力。实验结果表明：新提出的算法明显快于基本蚁群算法，佳点集杂交算子对解的优化有较好的作用。但需要继续探索避免陷入局部最优的方法，以及算法各部分所采用的方法的平衡问题
Abstract:
Basic ant colony algorithm has strong robusmess, but has slow convergence and easily be trapped in a local optimum. Aiming at these disadvantnges, by restricting the searching space of ants in a different size small window, has a big decrease of the searching time. By a good-point set genetic operator is introduced into the optimizing of solution, proposes an ant colony algorithm with good- point crossover operator based on different size window, exploring the ability of searching best solution of ACA in essential. Experiment shows that new algorithm is obviously fast than basic ant algorithm, and good point crossover operator is benefit to optimization of solution. But it need to further explore the metthad of avoiding trapping into local optimum, and the balance of method, which is used in every part of algorithm

相似文献/References:

[1]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.
[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]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.
[4]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.
[5]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.
[6]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.
[7]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.
[8]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.
[9]王为为 程家兴 贺晟.基于佳点集交叉的粒子群算法[J].计算机技术与发展,2009,(12):32.
　WANG Wei-wei,CHENG Jia-xing,HE Sheng.Particle Swarm Algorithm Based on Good Point Set Crossover[J].,2009,(12):32.
[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.

备注/Memo

备注/Memo:
安徽省教育科研项目（2006KJ088B）张然（1981-），男，安徽铜陵人，硕士研究生，研究方向为智能软件；贾瑞玉，副教授，研究方向为计算机图形学、数据挖掘、人工智能
更新日期/Last Update: 1900-01-01