模式识别与人工智能
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模式识别与人工智能  2013, Vol. 26 Issue (5): 425-431    DOI:
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收缩邻居节点集方法求解有向网络的最大流问题
赵姝,许显胜,华波,张燕平
安徽大学计算机科学与技术学院合肥230601
安徽大学计算智能与信号处理教育部重点实验室合肥230039
Contracting Neighbor-Node-Set Approach for Solving Maximum Flow Problem in Directed Network
ZHAO Shu,XU Xian-Sheng,HUA Bo,ZHANG Yan-Ping
School of Computer Science and Technology,Anhui University,Hefei 230601
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education,Anhui University,Hefei 230039

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摘要 最大流问题在许多领域有广泛的应用,然而随着网络规模的增加,传统的算法无法快速高效地求解最大流问题.对一个给定的有向网络,文中提出一种收缩邻居节点集的方法(CNA)求解其最大流.该方法通过收缩邻居节点集有效降低网络规模,使经典算法及改进算法可直接使用.首先给出收缩邻居节点集的条件,接着给出依据收缩条件构建目标网络的算法,最后利用经典算法求解目标网络的最大流以实现初始网络最大流的最优近似.实验结果表明CNA不仅平均能将目标网络的规模降至初始网络的一半,且能以较小的误差求得初始网络的最大流.
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Abstract:Maximum flow problem is widely applied in many fields. However,with the significant increase of network size,classic algorithms cannot solve maximum flow quickly and efficiently. In this paper,a method named Contracting Neighbor-node-set Approach (CNA) is presented to get its maximum flow approximately in a given directed flow network. Aiming at reducing the size of network,the method contracts some nodes and edges so that the classic algorithms can be used directly to approximately solve maximum flow problem with less time complexity. Firstly,the condition of contracting neighbor-node-set is given. Then,the algorithm is presented to construct the target network. Finally,the classic algorithms are applied on the target network to approximately get maximum flow of original network. The experimental results show that CNA not only obtains the maximum flow of original network with few errors,but also reduces the scale of the target flow network to half size of the original flow network averagely.
收稿日期: 2012-06-19     
ZTFLH: TP301.6  
  TP393  
基金资助:国家自然基金项目(No.61073117,61175046)、国家973计划项目(No.2007CB311003)、安徽省自然基金项目(No.11040606M)、安徽省高等学校省级自然科学基金项目(No.KJ2013A06)资助
作者简介: 赵姝,女,1979年生,博士,副教授,主要研究方向为机器学习、神经网络、商空间理论等.E-mail:zhaoshuzs2002@hotmail.com.许显胜,男,1987年生,硕士,主要研究方向为网络优化、社交网络.华波,男,1985年生,硕士,主要研究方向为机器学习、智能计算.张燕平(通讯作者),女,1962年生,教授,博士生导师,主要研究方向为机器学习、神经网络、商空间理论等.E-mail:zhangyp2@gmail.com.
引用本文:   
赵姝,许显胜,华波,张燕平. 收缩邻居节点集方法求解有向网络的最大流问题[J]. 模式识别与人工智能, 2013, 26(5): 425-431. ZHAO Shu,XU Xian-Sheng,HUA Bo,ZHANG Yan-Ping. Contracting Neighbor-Node-Set Approach for Solving Maximum Flow Problem in Directed Network. , 2013, 26(5): 425-431.
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