A learning approach to the bandwidth multicolouring problem

In this article, a generalisation of the vertex colouring problem known as bandwidth multicolouring problem (BMCP), in which a set of colours is assigned to each vertex such that the difference between the colours, assigned to each vertex and its neighbours, is by no means less than a predefined threshold, is considered. It is shown that the proposed method can be applied to solve the bandwidth colouring problem (BCP) as well. BMCP is known to be NP-hard in graph theory, and so a large number of approximation solutions, as well as exact algorithms, have been proposed to solve it. In this article, two learning automata-based approximation algorithms are proposed for estimating a near-optimal solution to the BMCP. We show, for the first proposed algorithm, that by choosing a proper learning rate, the algorithm finds the optimal solution with a probability close enough to unity. Moreover, we compute the worst-case time complexity of the first algorithm for finding a 1/(1–?) optimal solution to the given problem. The main advantage of this method is that a trade-off between the running time of algorithm and the colour set size (colouring optimality) can be made, by a proper choice of the learning rate also. Finally, it is shown that the running time of the proposed algorithm is independent of the graph size, and so it is a scalable algorithm for large graphs. The second proposed algorithm is compared with some well-known colouring algorithms and the results show the efficiency of the proposed algorithm in terms of the colour set size and running time of algorithm.     

网络虚拟化环境下的服务故障诊断算法

网络虚拟化环境下,底层网络的透明性造成虚拟网提供商不能诊断所有的虚拟网服务故障.为解决此问题,提出了基于映射关系的虚拟网服务故障传播模型.针对故障传播模型中故障集与症状集较大、网络环境动态和噪声大而导致的已有诊断算法误报率高、时间复杂度高的问题,基于网络虚拟化环境下症状内在相关性特点,提出了一种新的基于症状内在相关性的虚拟网服务故障诊断算法SFDoIC(service fault diagnosis algorithm based on inherent correlation among symptoms).仿真实验结果表明,SFDoIC算法能够很好地解决底层网络透明性造成的虚拟网服务故障难以定位的问题.SFDoIC算法可以有效地降低诊断算法的误报率,显著缩短诊断算法的运行时间.     

Experimenting with pattern-matching algorithms

Two new pattern-matching algorithms based on the Boyer-Moore algorithm are presented. Their performance is compared to that of earlier relevant variants in terms of the number of character comparisons and the required running time by exhaustive simulation. Experimental results show the efficiency of both these two new algorithms.     

Time- and storage-efficient implementation of an optimal planar convex hull algorithm

An implementation of an algorithm for computing the convex hull of a finite planar set of points is presented. The program is compared with an algorithm for the same purpose coded previously. Experimental results indicate that our program is superior to the other in terms of both running time and storage requirements.     

Analysis of FPTASes for the multi-objective shortest path problem

We propose a new FPTAS for the multi-objective shortest path problem with non-negative and integer arc costs. The algorithm uses elements from both an exact labeling algorithm and an FPTAS proposed by Tsaggouris and Zaroliagis [25]. We analyze the running times of these three algorithms both from a theoretical and a computational point of view. Theoretically, we show that there are instances for which the new FPTAS runs arbitrarily faster than the other two algorithms. Furthermore, for the bi-objective case, the number of approximate solutions generated by the proposed FPTAS is at most the number of Pareto-optimal points multiplied by the number of nodes. By performing a set of computational tests, we show that the new FPTAS performs best in terms of running time in case there are many dominated paths and the number of Pareto-optimal points is not too small.     

Parallel genetic algorithm with parameter adaptation

This paper presents an adaptive algorithm that can adjust parameters of a genetic algorithm according to the observed performance. The parameter adaptation occurs in parallel to the running of the genetic algorithm. The proposed method is compared with the algorithms that use random parameter sets and a standard parameter set. The experimental results show that the proposed method offers two advantages over the other competing methods: the reliability in finding the optimal solution and the time required for finding the optimal solution.     

成本控制下的快速影响最大化算法

针对成本控制下影响最大化时间复杂度高的问题,提出一种快速的最大化算法BCIM。首先提出对初始节点进行多次传播的传播模型;其次选择高影响力节点作为备用种子,并基于近距离影响减少计算节点影响范围的工作量;最后利用动态规划方法在每组备用种子中最多选择一个种子。仿真实验表明,与随机算法Random、每轮取影响力增量最大的节点的贪心算法Greedy_MII、每轮取影响力增量与成本比值最大的节点的贪心算法Greedy_MICR相比,在影响范围上,BICM接近或优于Greedy_MICR及Greedy_MII,远次于Random;在种子集合的质量上,BCIM、Greedy_MICR、Greedy_MII三者差距较小,但都远远好于Random;在运行时间上,BCIM是Random的几倍,而两个贪心算法都是BCIM的几百倍。BCIM算法能在较短时间内找到更有效的种子集合。     

基于列表结构的加权可擦除项集挖掘算法

可擦除项集挖掘是从大规模产品数据库中挖掘出低利润项集,以解决厂商财务危机的方法。传统挖掘方法只处理静态产品数据库,在提取可擦除项集时忽略项本身的权值。为解决现有可擦除项集挖掘算法考虑条件单一、效率低下的问题,提出一种有效的在增量数据集上挖掘加权可擦除项集的算法WELI。该算法综合考虑了数据不断积累和项具有不同重要性的因素,采用简洁的列表结构减少内存消耗,利用权重条件进行项集修剪,并结合包含索引和差集思想简化增益的计算过程,以实现高效的增量挖掘操作。实验表明：就运行时间和内存消耗而言,该算法在稠密数据集和稀疏数据集上均具有良好的实验效果,就可伸缩性而言,该算法也优于以往算法。     

Virtual Memory Behavior of Some Sorting Algorithms

Experimnental results are given about the performance of six sorting algorithms in a virtual memory based on the working set principle. With one exception, the algorithms are general internal sorting algorithms and not especially tuned for virtual memory. Algorithms are compared in terms of their time requirements, space requirements, and space-time integrals. The relative performances of the algorithms vary from one measure to the other. Especially in terms of a space-time integral, quicksort turns out to be the best algorithm, also in a working set virtual memory environment.     

Hadoop下资源匹配最大集作业调度算法

针对目前层级队列作业调度算法中资源占比高的作业执行效率低的问题,提出一种资源匹配最大集算法。该算法分析作业特征,引入完成度、等待时间、优先级、重调度次数为紧迫值因子,优先考虑资源占比高或等待时间长的作业,以改善作业公平性;采用双队列结构在可用资源总量内优先选择高紧迫值作业,在不同资源占比作业集比较中选择作业数最大集,以实现调度平衡。在与最大最小公平(Max-min fairness)算法的实例对比中发现,该算法可降低作业集平均等待时间、提高资源利用率。实验对比结果表明,该算法可将不同资源占比的单一类型作业集执行时间缩短18.73%,其中资源占比高的作业执行时间缩短27.26%;在混合型作业集中对应的执行时间可分别缩短22.36%与30.28%。所提算法能有效减少资源占比高作业的等待,提高作业整体执行效率。     

Benchmarking and Comparison of the Task Graph Scheduling Algorithms

The problem of scheduling a parallel program represented by a weighted directed acyclic graph (DAG) to a set of homogeneous processors for minimizing the completion time of the program has been extensively studied. The NP-completeness of the problem has stimulated researchers to propose a myriad of heuristic algorithms. While most of these algorithms are reported to be efficient, it is not clear how they compare against each other. A meaningful performance evaluation and comparison of these algorithms is a complex task and it must take into account a number of issues. First, most scheduling algorithms are based upon diverse assumptions, making the performance comparison rather meaningless. Second, there does not exist a standard set of benchmarks to examine these algorithms. Third, most algorithms are evaluated using small problem sizes, and, therefore, their scalability is unknown. In this paper, we first provide a taxonomy for classifying various algorithms into distinct categories according to their assumptions and functionalities. We then propose a set of benchmarks that are based on diverse structures and are not biased toward a particular scheduling technique. We have implemented 15 scheduling algorithms and compared them on a common platform by using the proposed benchmarks, as well as by varying important problem parameters. We interpret the results based upon the design philosophies and principles behind these algorithms, drawing inferences why some algorithms perform better than others. We also propose a performance measure called scheduling scalability (SS) that captures the collective effectiveness of a scheduling algorithm in terms of its solution quality, the number of processors used, and the running time.     

连续时间区间内的频繁词序列挖掘算法

查询文本中频繁出现的短语可快速掌握文本内容,然而传统频繁词序列挖掘算法面向挖掘任务时的时间复杂度较高,无法满足频繁更换查询条件及快速获得反馈的查询需求。利用基于频率树的快速频繁词序列挖掘算法（TS_Mining）,在保持后缀树线性构造时间的情况下实现文本集合中频繁词序列的查询,并采用树型索引结构避免多次扫描文本集合,降低算法时间复杂度。针对连续时间区间内的频繁词序列查询问题,提出改进的剪枝挖掘算法（TS_Pruning）,通过减少频率树的扫描范围进一步提高挖掘效率。实验结果表明,TS_Mining与TS_Pruning算法的运行时间相比经典Apriori挖掘算法约减少了2个数量级,具有更高的频繁词序列挖掘效率。     

基于种子候选的贪心策略影响力最大化算法

爬山贪心算法的时间复杂度较高,不易扩展至大规模社会网络.为了解决此问题,文中从理论上分析节点集影响力评估可转化为局部概率解计算,能够提高算法运行效率.将局部概率解函数拓展到贪心算法中,提出基于种子候选的贪心影响力最大化算法和基于种子候选的偷懒贪心影响力最大化算法.在4个真实数据集上实验表明,文中算法与具有成本效益的惰性前向选择算法(CELF)性能一致,但在运行时间上快于CELF.     

权重自适应clonal选择算法及其应用研究

在求解两个目标以上的多目标优化问题时,基于Pareto支配的多目标进化算法多数需要较长的求解时间.基于固定权重的聚合函数方法求解速度快,但要确定一个适合待求解问题的合理权重是十分困难的,为了解决这一问题,将clonal选择算法与权重自适应方法相结合,提出了一种适用于多目标优化问题的权重自适应clonal选择算法.并将权...     

基于时间条件提取序列的数据流偏好查询

传统关于偏好推理、偏好查询的研究主要集中在对关系元组表示的单个对象的偏好上,而将时间条件偏好查询的方法扩展到数据流的提取序列中是一个挑战,遇到的问题主要包括对数据流中序列的提取、快速处理以得到占优序列和占优对象等.针对偏好数据流,首先,扩展了连续查询语言(CQL),提出专门为有效处理数据流上的时间条件偏好的查询语言St...     

基于网格对应的双约束特征点匹配算法

常用的特征点匹配算法通常设置严苛的阈值以剔除错误匹配,但这样也会导致过多的正确匹配被删除。针对这一问题,提出了一种采用双约束的特征点匹配方法。首先,在局部上统计特征点匹配数量,运用网格对应的方法过滤部分错误匹配;然后,在全局上运用RANSAC方法计算基础矩阵,通过极线约束对匹配进行再一次筛选。实验表明,相比于传统的匹配算法,该算法能在不增加算法运行时间的前提下,获得更高数量和更高质量的匹配集合。     

Fully dynamic algorithms for permutation graph coloring

We introduce a dynamic model for maintaining permutation graph coloring. Our motivation comes from the strait type river routing problem in VLSI. This paper presents fully dynamic algorithms for the permutation graph coloring problem. These algorithms are designed to handle Insert and Delete operations and answer some queries. The aim is to provide for running times that are asymptotically more efficient than recomputation (off-line algorithms that run in 0(n logw) time, are known [5,6,10,3]). First, the algorithm A^ that runs in 0(n) uniform running time per Insert/Delete operation is presented. Second, a more sophisticated data structure leads to the algorithm A2 that runs in (9(m logw) uniform running time per Insert I Delete, where m denotes the number of chains in the decomposition. It follows from [7,4] that the running time of A2 when the points from the dynamically changing set are drawn independently from a uniform distribution on the unit square is G(yfn logn) per Insert/Delete in probability. Third, we sketch a composite algorithm A3 that switches between A± and A2 guarantees an amortized running time of (min{n,m logw)) per Insert/Delete. Finally, we outline a number of applications     

基于反向可达集的影响力最大化算法

现有影响力最大化算法多数因时间复杂度较高或影响力传播范围有限,不适用于大规模社交网络。基于独立级联模型,结合反向可达集采样提出一种改进的影响力最大化算法D-RIS。在影响力传播函数满足单调性和子模性的前提下,通过自动调试确定反向可达集生成数量的临界值。在Slashdot和Epinions真实数据集上的实验结果表明,D-RIS算法在影响力传播范围上接近CELF算法且优于RIS、HighDegree、LIR和pBmH启发式算法,同时在运行时间上相比CELF算法减少近百倍,具有更好的通用性与稳定性,适用于拓扑结构变化和规模较大的社交网络。     

划痕图像的连分式插值修补算法

目的 图像修复在图像处理中起着举足轻重的地位,针对目前大部分图像修补算法在修复划痕时存在纹理修复不够突出的缺陷,提出了两种基于连分式插值的修补算法,可以较好保持图像纹理的特性。方法 该算法基于连分式插值理论,采用图像破损点周围像素信息来插值出破损点的像素值。根据插值函数和插值窗口的不同,提出了两种插值方法,即Thiele型修补算法与Newton-Thiele型修补算法,解决不同纹理类型图像的划痕修补问题,并对插值过程中出现的奇异点问题和平移问题提出了行之有效的解决办法。结果 对大量的划痕图像进行实验测试,并通过主观评价和客观评价进行评估。客观评价包括峰值信噪比（PSNR）和运行时间的比较。相对于目前流行的一些修补方法来说,本文算法有更好的视觉效果,更高的峰值信噪比和更短的运行时间,峰值信噪比为44.79 dB,运行时间为0.53 s。结论 Thiele型修补算法更加擅长处理纹理垂直于划痕的图像,而Newton-Thiele型修补算法适用于复杂纹理的图像。     

基于多尺度高斯核的分布式正则化回归学习算法

针对工业、信息等领域出现的基于较大规模、非平稳变化复杂数据的回归问题,已有算法在计算成本及拟合效果方面无法同时满足要求.因此,文中提出基于多尺度高斯核的分布式正则化回归学习算法.算法中的假设空间为多个具有不同尺度的高斯核生成的再生核Hilbert空间的和空间.考虑到整个数据集划分的不同互斥子集波动程度不同,建立不同组合系数核函数逼近模型.利用最小二乘正则化方法同时独立求解各逼近模型.最后,通过对所得的各个局部估计子加权合成得到整体逼近模型.在2个模拟数据集和4个真实数据集上的实验表明,文中算法既能保证较优的拟合性能,又能降低运行时间.