Distributed evolutionary multi-objective mesh-partitioning algorithm for parallel finite element computations

Majority of the mesh-partitioning algorithms attempt to optimise the interprocessor communications, while balancing the computational load among the processors. However, it is desirable to simultaneously optimise the submesh aspect ratios in order to significantly improve the convergence characteristics of the domain decomposition based Preconditioned-conjugate-gradient algorithms, being used extensively in the state-of-the-art parallel finite element codes. Keeping this in view, a new distributed multi-objective mesh-partitioning algorithm using evolutionary computing techniques is proposed in this paper. Effectiveness of the proposed distributed mesh-partitioning algorithm is demonstrated by solving several unstructured meshes of practical-engineering problems and also benchmark problems.     

Approximation algorithms for the Weighted t-Uniform Sparsest Cut and some other graph partitioning problems

We study the Weighted t-Uniform Sparsest Cut (Weighted t-USC) and other related problems. In an instance of the Weighted t-USC problem, a parameter t and an undirected graph $G=(V,E)$ with edge-weights $w:E\to {\mathbb{R}}_{\ge 0}$ and vertex-weights $\eta :V\to {\mathbb{R}}_{+}$ are given. The goal is to find a vertex set $S\subseteq V$ with $|S|\le t$ while minimizing $w(S,V\backslash S)/\eta (S)$, where $w(S,V\backslash S)$ is the total weight of the edges with exactly one endpoint in S and $\eta (S)={\sum }_{v\in S}\eta (v)$. For this problem, we present a $(O(\log t),1+ϵ)$ factor bicriteria approximation algorithm. Our algorithm outperforms the current best algorithm when $t=n^{o(1)}$. We also present better approximation algorithms for Weighted ρ-Unbalanced Cut and Min–Max k-Partitioning problems.     

基于最小割图分割的社区发现算法

该文证明了模块度最大化问题可以被转换成为原网络上的最小割图分割问题,并且基于该证明提出了一种高效的社区发现算法。同时,该文创新性地将模块度理论与当今比较流行的统计推理模型相结合: 首先,这些统计推理模型被转化为模块度最大化问题中的零模型;其次,统计推理模型中的目标函数被修改并应用于本文的最优化算法中。实验结果显示,无论是在真实世界网络还是在人工生成网络中,该文提出的算法均具有高效和稳定的发现社区的能力。     

基于修正变分法开孔板和加强板结构的自由振动分析

为研究船舶开孔板和加强板结构的振动特性,用1阶剪切变形板理论描述各向同性板的位移场,并采用修正变分原理和区域分解方法建立板的离散动力学模型.每一块子域板的位移和转角分量通过第一类切比雪夫正交多项式展开.针对加强板模型,将该方法获得的结果与已经发表的文献和有限元商用软件计算结果进行对比,验证该方法的收敛性和正确性.基于修正变分法探讨多种开孔和加强板模型的自由振动特性,充分说明该数理模型和半解析方法是一种适合处理复杂板结构问题的数值工具.     

无线传感器网络中的Skyline查询处理技术

Skyline查询作为多目标决策的重要手段之一,在无线传感器网络应用中发挥着越来越重要的作用。对无线传感器网络中的Skyline查询处理技术进行了论述。首先讨论集中数据库中的Skyline查询算法。其次,讨论无线传感器网络中Skyline查询的典型应用。进而,根据无线传感器网络能量、存储和处理能力有限等特点,全面论述了无线传感器网络中的Skyline查询方法,并指出了今后的研究方向。     

求解VLSI 电路划分问题的混合粒子群优化算法

电路划分是VLSI物理设计过程中的一个关键阶段.该问题本质上是一个NP困难的组合优化问题.针对该问题,提出了一种带FM策略的混合粒子群优化算法.引入遗传算法的两点交叉算子和随机两点交换变异算子,保证了粒子在位置更新后依然可行;为了提高算法的局部搜索能力,将具有较强局部搜索能力的FM策略融入算法的位置更新;设计了种群多样性变异策略,提高了种群多样性,避免了易陷入局部最优的缺陷.对ISCAS89标准测试电路的仿真实验结果表明,所构造的算法是有效的.     

运钞车车辆路径规划策略

针对实际运钞网点数每天动态变化问题,提出一种先划分、再优化的动态运钞车路线规划策略。第一阶段先采用Dijkstra算法求出两点之间的最短路径,再利用最近邻算法和均衡工作量因子求出动态需求车辆的车辆数和每条路径上的网点;第二阶段利用前置交叉的改进遗传算法,分别优化每条路径并求出每条路径上的网点顺序,获得距离最短和时间最少的路径。实验结果表明,该策略能有效解决车辆数目和路径根据需求动态变化的问题,达到节约和合理利用资源的目的。     

片上多核Cache资源管理机制研究

随着片上多核成为处理器发展的主流和片上Cache资源的持续增长,Cache资源的管理已成为片上多核的关键问题。介绍了片上多核Cache资源管理的研究进展,依据研究内容将Cache资源的管理分为Cache划分和Cache共享两类。对Cache划分,探讨了其主要组成部分和一般形式,分析和比较了典型的片上多核Cache划分机制。对Cache共享,给出了其主要研究内容,并介绍和比较了几种主流的片上多核Cache共享机制。通过分析,认为软硬件协同管理的页划分应是未来片上多核Cache划分机制的研究重点;而片上多核Cache共享机制的研究则应从目标应用的Cache行为特征着手。     

Approaches for scaling DBSCAN algorithm to large spatial databases

The huge amount of information stored in datablases owned by coporations(e.g.retail,financial,telecom) has spurred a tremendous interest in the area of knowledge discovery and data mining.Clustering.in data mining,is a useful technique for discovering intersting data distributions and patterns in the underlying data,and has many application fields,such as statistical data analysis,pattern recognition,image processsing,and other business application,s Although researchers have been working on clustering algorithms for decades,and a lot of algorithms for clustering have been developed,there is still no efficient algorithm for clustering very large databases and high dimensional data,As an outstanding representative of clustering algorithms,DBSCAN algorithm shows good performance in spatial data clustering.However,for large spatial databases,DBSCAN requires large volume of memory supprot and could incur substatial I/O costs because it operates directly on the entrie database,In this paper,several approaches are proposed to scale DBSCAN algorithm to large spatial databases.To begin with,a fast DBSCAN algorithm is developed.which considerably speeeds up the original DBSCAN algorithm,Then a sampling based DBSCAN algorithm,a partitioning-based DBSCAN algorithm,and a parallel DBSCAN algorithm are introduced consecutively.Following that ,based on the above-proposed algorithms,a synthetic algorithm is also given,Finally,some experimental results are given to demonstrate the effectiveness and efficiency of these algorithms.     

基于划分策略和填充曲线的抖动模式研究

抖动处理是计算机图形学和图像处理中的一项关键技术,抖动算法的好坏直接关系互图像打印质量的优劣,针对利用评价函数生成较大抖动模式比较困难的问题,提出了划分初始模式的方法,并渐进生成较大抖动模式,针对模式抖动中出现的边界赝象,引入填充曲线机制加以消除,实验表明,采用以上方法得到的结果比较令人满意。