分布式计算中的搜索树技术研究

面向范例的分布式计算是一种新型分布式计算环境,它基于移动代理系统使用范例来实现分布式计算.这些范例提供了用于通信的下层结构,应用程序员只需通过图形界面来提供明确的应用功能.该分布式计算环境支持5个公共范例:任务包范例、搜索树范例、继承编程范例、有限差范例以及个体仿真范例.以搜索树范例为例,描述了搜索树的基本结构,并对其进行分布式实现,阐明了面向范例的分布式计算的具体实现方法.     

Theoretical comparisons of search strategies in branch-and-bound algorithms

Four known search strategies used in branch-and-bound algorithms-heuristic search, depth-first search, best-bound search, and breadth-first search-are theoretically compared from the viewpoint of the performance of the resulting algorithms. Heuristic search includes the other three as special cases. Since heuristic search is determined by a heuristic functionh, we first investigate how the performance of the resulting algorithms depends onh. In particular, we show that heuristic search is stable in the sense that a slight change inh causes only a slight change in its performance. The best and the worst heurstic functions are clarified, and also discussed is how the heuristic functionh should be modified to obtain a branch-and-bound algorithm with an improved performance. Finally, properties and limitations of depth-first search, best-bound search, and breadth-first search viewed as special cases of heuristic search are considered. In particular, it is shown that the stability observed for heuristic search no longer holds for depth-first search.     

A new branch-and-bound algorithm for standard quadratic programming problems

In this paper we propose convex and LP bounds for standard quadratic programming (StQP) problems and employ them within a branch-and-bound approach. We first compare different bounding strategies for StQPs in terms both of the quality of the bound and of the computation times. It turns out that the polyhedral bounding strategy is the best one to be used within a branch-and-bound scheme. Indeed, it guarantees a good quality of the bound at the expense of a very limited computation time. The proposed branch-and-bound algorithm performs an implicit enumeration of all the KKT (stationary) points of the problem. We compare different branching strategies exploiting the structure of the problem. Numerical results on randomly generated problems (with varying density of the underlying convexity graph) are reported which show the effectiveness of the proposed approach, in particular in limiting the growth of the number of nodes in the branch-and-bound tree as the density of the underlying graph increases.     

Randomised restarted search in ILP

Recent statistical performance studies of search algorithms in difficult combinatorial problems have demonstrated the benefits of randomising and restarting the search procedure. Specifically, it has been found that if the search cost distribution of the non-restarted randomised search exhibits a slower-than-exponential decay (that is, a “heavy tail”), restarts can reduce the search cost expectation. We report on an empirical study of randomised restarted search in ILP. Our experiments conducted on a high-performance distributed computing platform provide an extensive statistical performance sample of five search algorithms operating on two principally different classes of ILP problems, one represented by an artificially generated graph problem and the other by three traditional classification benchmarks (mutagenicity, carcinogenicity, finite element mesh design). The sample allows us to (1) estimate the conditional expected value of the search cost (measured by the total number of clauses explored) given the minimum clause score required and a “cutoff” value (the number of clauses examined before the search is restarted), (2) estimate the conditional expected clause score given the cutoff value and the invested search cost, and (3) compare the performance of randomised restarted search strategies to a deterministic non-restarted search. Our findings indicate striking similarities across the five search algorithms and the four domains, in terms of the basic trends of both the statistics (1) and (2). Also, we observe that the cutoff value is critical for the performance of the search algorithm, and using its optimal value in a randomised restarted search may decrease the mean search cost (by several orders of magnitude) or increase the mean achieved score significantly with respect to that obtained with a deterministic non-restarted search. Editors: Rui Camacho     

Depth-m search in branch-and-bound algorithms

A new search strategy, called depth-m search, is proposed for branch-and-bound algorithms, wherem is a parameter to be set by the user. In particular, depth-1 search is equivalent to the conventional depth-first search, and depth- search is equivalent to the general heuristic search (including best-bound search as a special case). It is confirmed by computational experiment that the performance of depth-m search continuously changes from that, of depth-first search to that of heuristic search, whenm is changed from 1 to . The exact upper bound on the size of the required memory space is derived and shown to be bounded byO(n^m), wheren is the problem size. Some methods for controllingm during computation are also proposed and compared, to carry out the entire computation within a given memory space bound.     

使用R树进行k-NN搜索

在地理信息系统中经常要做k-NN搜索，进行这些查询用到的算法与位置和范围查询的算法不同，需要专门进行研究，介绍了一种分支界限遍历R树算法，并将该算法概括为k-NN算法。文中讨论了两种方法。对R树进行结点内MBR的排序以及剪枝过程，以减少搜索空间中需访问结点的数量，有效地进行k-NN搜索。     

Meta-interpreters and partial evaluation in Parlog

Since logic programs are executable specifications, the main concern of logic programming, producing efficient programs, is tangential to the mainstream of formal methods. A fashionable automatic transformation technique for producing efficient programs, partial evaluation, applied to the concurrent logic programming language Parlog is discussed.     

Parallel and distributed local search in COMET

The availability of commodity multiprocessors and high-speed networks of workstations offer significant opportunities for addressing the increasing computational requirements of optimization applications. To leverage these potential benefits, it is important, however, to make parallel and distributed processing easily accessible to a wide audience of optimization programmers. This paper addresses this challenge by proposing parallel and distributed programming abstractions that keep the distance from sequential local search algorithms as small as possible. The abstractions, including parallel loops, interruptions, thread pools, and shared objects, are compositional and cleanly separate the optimization program and the parallel instructions. They have been evaluated experimentally on a variety of applications, including warehouse location and coloring, for which they provide significant speedups.     

Fuzzy branch-and-bound algorithm for flow shop scheduling

Scheduling is the allocation of resources over time to perform a collection of task. It is an important subject of production and operations management area. For most of scheduling problems made so far, the processing times of each job on each machine and due dates have been assigned as a real number. However in the real world, information is often ambiguous or imprecise. In this paper fuzzy concept are applied to the flow shop scheduling problems. The branch-and-bound algorithm of Ignall and Schrage was modified and rewritten for three-machine flow shop problems with fuzzy processing time. Fuzzy arithmetic on fuzzy numbers is used to determine the minimum completion time (C _max). Proposed algorithm gets a scheduling result with a membership function for the final completion time. With this membership function determined, a wider point of view is provided for the manager about the optimal schedule.     

几乎最快与渐近最优的并行分枝界限算法

分枝界限算法是求解组合优化问题的技术之一,它被广泛地应用在埃运筹学与组合数学中.对共享存储的最优优先一般并行分枝界限算法给出了运行时间复杂度下界Ω(m/p+hlogp),其中p为可用处理器数,h为扩展的结点数,m为状态空间中的活结点数.通过将共享存器设计成p个立体堆,提出了PRAM-EREW上一个新的一般并行分枝界限算法,理论上证明了对于h<p^2p,该算法为最快且渐近最优的并行分枝界限算法.最后对0-r背包问题给出了模拟实验结果.     

基于双禁忌对象的并行禁忌搜索算法

禁忌搜索算法是解决组合优化问题的一种主要方法,是克服NP完全问题的一个有效途径。随着计算网格的发展,将禁忌搜索算法引入到这种分布式并行计算环境中,具有广泛的应用价值。提出了一个基于双禁忌对象的禁忌搜索算法,在此算法的基础上,利用并行化分散搜索策略来提高算法的求解精度。实验结果表明该并行禁忌搜索算法性能较高。     

基于分支定界法的关键链项目计划重排*

通过举例说明在关键链项目计划中插入输入缓冲后,可能出现资源冲突和紧前关系冲突。从局部和全局两个方面采用分支定界法对项目计划进行重排来解决冲突问题。通过举例说明基于分支定界法的计划重排算法是有效和可行的。通过模拟仿真,从三个不同层次分析项目活动任务的不确定性对项目完工率和项目惩罚成本的影响。结果显示,保留原始关键链的基于分支定界法的全局性关键链计划重排方法较之其他方法要好,不确定性更低,项目的完工率更高,项目的惩罚成本更低。     

Efficient parallel branch-and-bound algorithm for constructing minimum ultrametric trees

The construction of evolutionary trees is important for computational biology, especially for the development of biological taxonomies. The ultrametric tree (UT) is a commonly used model for evolutionary trees assuming that the rate of evolution is constant (molecular clock hypothesis). However, the construction of minimum ultrametric trees (MUTs, principle of minimum evolution) has been shown to be NP-hard even from a metric distance matrix. The branch-and-bound algorithm is generally used to solve a wide variety of NP-hard problems. In previous work, a sequential branch-and-bound algorithm for constructing MUTs (BBU) was presented and the experimental results showed that it is useful for MUT construction. Hence, in this study, an efficient parallel branch-and-bound algorithm (PBBU) for constructing MUTs or near-MUTs from a metric distance matrix was designed. A random data set as well as some practical data sets of Human + Chimpanzee Mitochondrial and Bacteriophage T7 DNAs were used to test the PBBU. The experimental results show that the PBBU found an optimal solution for 36 species on 16 PCs within a reasonable time. To the best of our knowledge, no algorithm has been found to solve this problem even for 25 species. Moreover, the PBBU achieved satisfying speed-up ratios for most of the test cases.     

Sequential quadratic programming enhanced backtracking search algorithm

In this paper, we propose a new hybrid method called SQPBSA which combines backtracking search optimization algorithm (BSA) and sequential quadratic programming (SQP). BSA, as an exploration search engine, gives a good direction to the global optimal region, while SQP is used as a local search technique to exploit the optimal solution. The experiments are carried on two suits of 28 functions proposed in the CEC-2013 competitions to verify the performance of SQPBSA. The results indicate the proposed method is effective and competitive.     

自适应布谷鸟搜索的并行K-means聚类算法*

针对K-means聚类算法受初始类中心影响,聚类结果容易陷入局部最优导致聚类准确率较低的问题,提出了一种基于自适应布谷鸟搜索的K-means 聚类改进算法,并利用MapReduce编程模型实现了改进算法的并行化。通过搭建的Hadoop分布式计算平台对不同样本数据集分别进行10次准确性实验和效率实验,结果表明：(1)聚类的平均准确率在实验所采用的4种UCI标准数据集上,相比原始K-means聚类算法和基于粒子群优化算法改进的K-means聚类算法都有所提高;(2) 聚类的平均运行效率在实验所采用的5种大小递增的随机数据集上,当数据量较大时,显著优于原始K-means串行算法,稍好于粒子群优化算法改进的并行K-means聚类算法。可以得出结论,在大数据情景下,应用该算法的聚类效果较好。     

A branch-and-price guided search approach to maritime inventory routing

We apply branch-and-price guided search to a real-world maritime inventory routing problem, in which the inventory of a single product, which is produced and consumed at multiple sites, and its transport, which is done with a heterogeneous fleet of vessels, is managed over a finite horizon. Computational experiments demonstrate that branch-and-price guided search quickly produces solutions that are near-optimal and of better quality than those produced by a state-of-the-art, commercial integer programming solver that is given much more time. We also develop local search schemes to further reduce the time needed to find high quality solutions and present computational evidence of their efficacy.