考虑布线资源松弛的X结构Steiner最小树算法

为了进一步考虑X结构,并充分利用障碍内可用布线资源,文中提出考虑布线资源松弛的X结构Steiner最小树算法.为了能够求解离散问题,在粒子的更新操作中引入交叉算子和变异算子.通过构建查找表,为整个算法流程提供快速的信息查询.提出角点选取策略,通过引入一些障碍角点,使粒子满足约束.最后构建精炼策略,进一步提高最终布线树的质量.实验表明,文中算法充分利用障碍内可用布线资源,有效缩短总布线长度,取得较佳的总布线长度.     

基于斯坦纳树的雷场网络大面积损坏修复策略

战场环境下预先设定的智能雷场网络易受到敌方攻击而导致大面积损坏,雷场网络被分割成数个互不相连的部分从而丧失了通讯功能。通过将问题映射到斯坦纳最小树问题,提出了一种新颖的雷场网络修复策略。首先采用雷场区域网格模型限制算法的搜索空间,随后引入蛙跳和离散量子粒子群混合优化(JF QDPSO)算法确定中续节点位置,修复受损网络。仿真实验表明该策略能够有效的恢复网络拓扑结构,算法计算较快,与其它算法相比,重建后的网络通信能耗小,网络生存周期长。     

A heuristic for constructing a rectilinear Steiner tree by reusing routing resources over obstacles

The obstacle-avoiding rectilinear Steiner minimal tree (OARSMT) problem is a hot topic in very-large-scale integration physical design. In practice, most of the obstacles occupy the device layer and certain lower metal layers. Therefore, we can place wires on top of the obstacles. To maximize routing resources over obstacles, we propose a heuristic for constructing a rectilinear Steiner tree with slew constraints. Our algorithm adopts an extended rectilinear full Steiner tree grid as the routing graph. We mark two types of Steiner point candidates, which are used for constructing Steiner trees and refining solutions. A shortest path heuristic variant is designed for constructing Steiner trees and it takes into account slew constraint by inhibiting growth. Furthermore, we use a pre-computed strategy to avoid calculating slew rate repeatedly. Experimental results show that our algorithm maximizes routing resources over obstacles and saves routing resources outside obstacles. Compared with the conventional OARSMT algorithm, our algorithm reduces the wire length outside obstacles by as much as 18.74% and total wire length by as much as 6.03%. Our algorithm improves the latest related algorithm by approximately 2% in terms of wire length within a reasonable running time. Additionally, calculating the slew rate only accounts for approximately 15% of the total runing time.     

基于高维空间典型样本Steiner最小树覆盖模型的一类分类算法

最小生成树数据描述方法在刻画高维空间样本点分布时,将所有图形的边作为新增虚拟样本以提供同类样本分布描述,这种描述存在分支多覆盖模型复杂,且局部覆盖不够合理的问题.针对该问题,依据特征空间中同类样本分布的连续性规律,提出基于高维空间典型样本Steiner最小树覆盖模型的一类分类算法,该算法首先对目标类训练集进行样本修剪,去除冗余信息和噪声信息,选择最具代表性的样本作为训练集,然后对保留的典型样本构建Steiner最小树覆盖模型.算法分析和仿真实验结果表明,相比最小生成树数据描述,文中提出的方法能在较低覆盖模型复杂度的前提下更合理的描述目标类样本空间分布,构建更合理的覆盖模型,在分类正确率和适用样本规模上都表现出一定的优越性.     

基于直角Steiner树的片上网络互连算法

提出了一种基于最小直角Steiner树,在Manhattan平面上避免障碍物的互连算法,以实现片上网络中各IP核的互连.该算法在定制NoC中将Steiner树的生成算法用于互连设计.算法首先在初始阶段对有障碍两点间的边权重重新赋值,然后调用最小生成树算法,使生成的直角Steiner树总长度最小.实验表明,该算法可以使片上网络的总连线缩短.     

Viral systems: A new bio-inspired optimisation approach

The paper presents a new approach to deal with combinatorial problems. It makes use of a biological analogy inspired by the performance of viruses. The replication mechanism, as well as the hosts’ infection processes is used to generate a metaheuristic that allows the obtention of valuable results. The viral system (VS) theoretical context is described and it is applied to a library of medium-to-large-sized cases of the Steiner problem for which the optimal solution is known. The method is compared with the metaheuristics that have provided the best results for the Steiner problem. The VS provides better solutions than genetic algorithms and certain tabu search approaches. For the most sophisticated tabu search approaches (the best metaheuristic approximations to the Steiner problem solution) VS provides solutions of similar quality.     

Relay sensor placement in wireless sensor networks

This paper addresses the following relay sensor placement problem: given the set of duty sensors in the plane and the upper bound of the transmission range, compute the minimum number of relay sensors such that the induced topology by all sensors is globally connected. This problem is motivated by practically considering the tradeoff among performance, lifetime, and cost when designing sensor networks. In our study, this problem is modelled by a NP-hard network optimization problem named Steiner Minimum Tree with Minimum number of Steiner Points and bounded edge length (SMT-MSP). In this paper, we propose two approximate algorithms, and conduct detailed performance analysis. The first algorithm has a performance ratio of 3 and the second has a performance ratio of 2.5. Xiuzhen Cheng is an Assistant Professor in the Department of Computer Science at the George Washington University. She received her MS and PhD degrees in Computer Science from the University of Minnesota - Twin Cities in 2000 and 2002, respectively. Her current research interests include Wireless and Mobile Computing, Sensor Networks, Wireless Security, Statistical Pattern Recognition, Approximation Algorithm Design and Analysis, and Computational Medicine. She is an editor for the International Journal on Ad Hoc and Ubiquitous Computing and the International Journal of Sensor Networks. Dr. Cheng is a member of IEEE and ACM. She received the National Science Foundation CAREER Award in 2004. Ding-Zhu Du received his M.S. degree in 1982 from Institute of Applied Mathematics, Chinese Academy of Sciences, and his Ph.D. degree in 1985 from the University of California at Santa Barbara. He worked at Mathematical Sciences Research Institutea, Berkeley in 1985-86, at MIT in 1986-87, and at Princeton University in 1990-91. He was an associate-professor/professor at Department of Computer Science and Engineering, University of Minnesota in 1991-2005, a professor at City University of Hong Kong in 1998-1999, a research professor at Institute of Applied Mathematics, Chinese Academy of Sciences in 1987-2002, and a Program Director at National Science Foundation of USA in 2002-2005. Currently, he is a professor at Department of Computer Science, University of Texas at Dallas and the Dean of Science at Xi’an Jiaotong University. His research interests include design and analysis of algorithms for combinatorial optimization problems in communication networks and bioinformatics. He has published more than 140 journal papers and 10 written books. He is the editor-in-chief of Journal of Combinatorial Optimization and book series on Network Theory and Applications. He is also in editorial boards of more than 15 journals. Lusheng Wang received his PhD degree from McMaster University in 1995. He is an associate professor at City University of Hong Kong. His research interests include networks, algorithms and Bioinformatics. He is a member of IEEE and IEEE Computer Society. Baogang Xu received his PhD degree from Shandong University in 1997. He is a professor at Nanjing Normal University. His research interests include graph theory and algorithms on graphs.     

基于关键节点和多播节点的多播路由算法

在视频会议等多播应用中，降低多播树网络费用非常重要。本文提出了基于关键节点和目的节点的多播路由KDDMC算法。由于在算法中优先考虑采用关键节点，实现更多链路的共享，从而降低网络费用。在随机网络上的仿真结果表明，KDDMC算法的多播树网络费用优于SPH算法。同时证明了KDDMC算法的复杂度为O(n^3)，且利用所提出的路由表算法易于分布式实现。     

图的Steiner最小树问题的降阶回溯算法

图的Steiner最小树问题是经典的组合优化问题,是一个NP难题,在不同的领域有着广泛的应用。研究该问题的部分数学性质,在此基础上给出了该问题的初步降阶方法和下界子方法,形成一个新的回溯算法。该算法具有较低的时间复杂度,还给出了应用实例及其分析。     

链路共享的时延约束组播路由局部平衡优化算法*

分析了构建时延约束组播树的代价和计算复杂度,从优化最短路径出发,提出了一种基于局部信息的链路共享平衡优化路由算法。算法设计的链路选择函数不仅考虑了目的节点的优先级,同时还考虑了给予低时延路径一定的优先权,在满足时延约束的情况下使组播树的链路数尽可能少,降低了通过最小时延路径建树的概率,提高了链路的共享性。仿真表明,算法的综合性能比较好,在代价、延迟和计算复杂度之间能获得较好的平衡。