边排序质量影响因素研究

网络可靠度BDD分析方法的计算性能与BDD尺度紧密相关,而BDD尺度严重依赖边排序质量。因此,边排序问题是网络可靠度BDD分析方法的重要问题。由于求解最优边排序是一个NP问题,在实际网络可靠度分析中,通常采用启发式边排序策略如BFS和DFS,它们适用不同类型的网络。然而,对于给定网络,采用何种边排序策略更优,有哪些因素影响边排序质量,迄今没有给出评判依据。利用边界集思想,提出"边界长度(BSL)"概念,并用边界长度BSL表征边排序质量,揭示边界长度BSL和BDD尺度(节点数目)之间的关系。实验结果表明,边界长度BSL与BDD尺度具有正相关性,即较小BSL对应的BDD尺度较小,较大BSL对应的BDD尺度较大,多数情况下,BSL取最值时,BDD尺度能取到(或接近)最值。这为特定网络选择(或设计)高性能边排序提供了重要的参考依据。     

优先级边排序策略及其性能分析

网络可靠度BDD分析的计算复杂度与BDD尺度线性相关,而BDD尺度依赖边排序策略,边排序问题是BDD网络可靠度分析的重要问题。从网络结构特性出发,设计了优先级边排序策略并深入研究了在该策略下不同排序起点对BDD尺度的影响。实验结果表明:源点和网络中心不是高性能排序起点,最佳排序起点分布在网络边缘,网络中心点为最差排序起点。该结论可为揭示边排序影响BDD尺度的本质以及研究高效启发性边排序策略提供重要参考依据。     

网络可靠性分析中自顶向下的二叉决策图构造研究

采用边界分区标识网络的思想,实现基于边界分区的自顶向下K端可靠度二叉决策图(BDD)构建算法。针对BDD构建过程中存在的节点冗余问题,提出无效边冗余消除和K点非连通冗余消除2种处理技术。在规则网络和实际工程中的实验结果表明,利用无效边冗余消除和K点非连通消除技术后的BDD改进算法,在不影响算法时间性能的情况下,可大幅缩减BDD尺度,提升K端网络可靠度分析算法性能,适用于大规模的网络可靠度分析。     

基于遗传禁忌混合策略的二叉判定图最小化算法研究

提出了一种新的动态启发式二叉判定图（BDD）最小化算法．该算法将遗传算法的全局搜索能力和禁忌搜索的邻域搜索策略相结合来寻找BDD的最优变量排序，以实现BDD结点规模最小化．实验结果表明该算法性能优于其它启发式算法．     

网络可靠度BDD分析算法的性能改进

BDD是布尔函数的图形表示形式,被广泛应用到网络可靠度的分析计算中。为了提升网络可靠度BDD分析算法的性能,本文根据边扩展图实例,识别两类无效边扩展路径:冗余节点型无效扩展路径和ST非连通型无效扩展路径,然后基于基本的网络可靠度BDD分析算法,实现了两类无效扩展路径的消除技术。实验结果表明,两种无效扩展路径消除技术能够提前识别无效扩展路径,避免无效扩展,有效减少中间子网的数量,缩减分析时间;通过把两种技术结合起来,可以有效地消除边扩展图中的这两类无效扩展路径,从而极大提升可靠度分析的性能。     

通过八数码问题比较搜索算法的性能

搜索算法的核心在于搜索策略的制定.一般的搜索算法采用无信息指导的搜索策略,如深度优先搜索(DFS)和宽度优先搜索(BFS),还有一些搜索算法采用了启发式信息指导的搜索策略,如A*算法.不同的搜索策略会使得搜索算法的性能有很大的差异.使用以上3种搜索算法实现八教码问题的求解,分析和比较三者所表现出来的性能,同时指出3种搜索算法的特点和应用范围,最后给出分析结论以指导开发和使用更加高效的搜索策略.     

状态空间搜索算法的进一步探索

主要讨论了BFS、DFS、A＊算法在状态空间搜索中的应用并且给出其在Mathematics下实现。在Mathematics中根据节点数据绘制出节点分布图,分别使用BFS、DFS、A＊搜索对给定的源点和目标点之间的路径进行搜索,并比较得到的路径耗散数据,说明引入启发式函数对搜索效率的影响。     

基于故障树的无线传感器网络可靠度符号计算

以无线传感器网络（WSN ）中应用通信可靠性（ACR）为背景,利用故障树模型中的事件元素与逻辑门元素,建立基于故障树的WSN可靠性结构。为降低WSN可靠度计算的复杂性,给出从WSN可靠性结构转换到二元决策图BDD结构的算法,利用BDD算法优化计算过程。以分层簇型网络中可用路径以及节点冗余下的应用通信可靠性问题为例,给出其可靠性结构,利用CUDD软件包给出用递归方法实现构建基于故障树的WSN可靠性结构的BDD算法,计算以上两种情况下的WSN可靠度。实验结果表明,该方法具有可行性。     

一种故障树向BDD的转化方法

针对故障树分析的关键技术—排序和置换,提出一种基于BDD的快速有效的（LNPC）方法。该方法采用制定的排序和置换策略直接完成子事件的排序与门事件的置换,一次性完成故障树到BDD的转化和优化,增加了获取最小规模BDD的排序机会,同时降低了BDD的存储空间且不需要先写出故障树的布尔函数。算法分析与实验结果表明该方法对不同的故障树转化是有效的。     

二元决策图BDD在话务故障分析中的应用①

针对移动网络通话问题日益严重,需要建立一个故障树分析（FTA）模型。利用BDD技术分析各个基本事件的结构重要度、概率重要度和临界值重要度。基本事件的排序对故障树生成的BDD节点个数有直接影响,以及节点的结构重要度和概率重要度都有影响。采用相邻底事件优先排序法,能够尽量减少BDD节点个数和对重要度的影响。利用BDD法比传统FTA法计算的重要度数值更接近,计算效率更高。     

Mining maximal hyperclique pattern: A hybrid search strategy

A hyperclique pattern is a new type of association pattern that contains items which are highly affiliated with each other. Specifically, the presence of an item in one transaction strongly implies the presence of every other item that belongs to the same hyperclique pattern. In this paper, we present an algorithm for mining maximal hyperclique patterns, which specifies a more compact representation of hyperclique patterns and are desirable for many applications, such as pattern-based clustering. Our algorithm exploits key advantages of both the Depth First Search (DFS) strategy and the Breadth First Search (BFS) strategy. Indeed, we adapt the equivalence pruning method, one of the most efficient pruning methods of the DFS strategy, into the process of the BFS strategy. Our experimental results show that the performance of our algorithm can be orders of magnitude faster than standard maximal frequent pattern mining algorithms, particularly at low levels of support.     

基于八方向跟踪算法的迷宫问题新解

本文提出了一个基于八方向跟踪算法的破解迷宫问题的新方法,避免了用深探法或广探法求解迷宫问题的诸多问题,它不仅为计算机的解题提供了一个快捷的算法,而且也为人工或机器人破解提供了一个无需记忆的简便方法。另外,本文还给出了迷宫次佳通路和最佳通路（即捷径）的求解算法;岔道剔除算法和最佳八连通选择算法。本文的所有方法尽管是针对求解单通路迷宫提出采的,但算法对多通路和有环的迷宫也同样有效。     

BD-ADOPT: a hybrid DCOP algorithm with best-first and depth-first search strategies

Distributed Constraint Optimization Problem (DCOP) is a promising framework for modeling a wide variety of multi-agent coordination problems. Best-First search (BFS) and Depth-First search (DFS) are two main search strategies used for search-based complete DCOP algorithms. Unfortunately, BFS often has to deal with a large number of solution reconstructions whereas DFS is unable to promptly prune sub-optimal branch. However, their weaknesses will be remedied if the two search strategies are combined based on agents’ positions in a pseudo-tree. Therefore, a hybrid DCOP algorithm with the combination of BFS and DFS, called BD-ADOPT, is proposed, in which a layering boundary is introduced to divide all agents into BFS-based agents and DFS-based agents. Furthermore, this paper gives a rule to find a suitable layering boundary with a new strategy for the agents near the boundary to realize the seamless joint between BFS and DFS strategies. Detailed experimental results show that BD-ADOPT outperforms some famous search-based complete DCOP algorithms on the benchmark problems.     

四种最短路径算法实例分析

通过理论分析,结合实际应用,在GIS节点数很大的数字地形图中,从完备性、最优性、时间复杂度、空间复杂度几种性能问题实例分析,较系统地总结出深度优先搜索(DFS)、广度优先搜索(BFS)、双向广度优先搜索(DBFS)、A★算法四种算法代价及优缺点.     

A hybrid evolutionary local search with depth first search split procedure for the heterogeneous vehicle routing problems

Routing Problems have been deeply studied over the last decades. Split procedures have proved their efficiency for those problems, especially within global optimization frameworks. The purpose is to build a feasible routing solution by splitting a giant tour into trips. This is done by computing a shortest path on an auxiliary graph built from the giant tour. One of the latest advances consists in handling extra resource constraints through the generation of labels on the nodes of the auxiliary graph. Lately, the development of a new generic split family based on a Depth First Search (DFS) approach during label generation has highlighted the efficiency of this new method for the routing problems, through extensive numerical evaluations on the location-routing problem.In this paper, we present a hybrid Evolutionary Local Search (hybrid ELS) for non-homogeneous fleet Vehicle Routing Problems (VRP) based on the application of split strategies. Experiments show our method is able to handle all known benchmarks, from Vehicle Fleet Mix Problems to Heterogeneous Fleet VRP (HVRP). We also propose a set of new realistic HVRP instances from 50 to more than 250 nodes coming from French counties. It relies on real distances in kilometers between towns. Since many classical HVRP instance sets are solved to optimality, this new set of instances could allow a fair comparative study of methods. The DFS split strategy shows its efficiency and attests the fact that it can be a promising line of research for routing problems including numerous additional constraints.     

Cooperative particle swarm optimizer with depth first search strategy for global optimization of multimodal functions

This paper presents a Cooperative Particle Swarm Optimizer with Depth First Search Strategy (DFS-CPSO), which has better seacrch capality than classical Particle Swarm Optimizer (PSO) in solving multimodal optimization problems. In order to improve the quality of information exchange, the Depth First Search (DFS) strategy is hybridized to Cooperative Particle Swarm Optimization(CPSO), which makes information transfer more effectively and generates better quality solution. Specifically, DFS strategy enables different components of solution vector to exchange information separately with PSO and increases the diversity of the population, so that the information of solution components could be preserved by multiple iterations in CPSO. Confirmatory experiments are performed to prove the effectiveness of employing the DFS strategy to CPSO. The comparative results demonstrate superior performance of DFS-CPSO in solving high dimensional multimodal functions than CPSO and other advanced methods.

     

基于改进的不交化最小路集的网络系统可靠性算法

本文根据不交化布尔代数及BDD原理提出了一种简化的求解不交化最小路集的改进算法。对最小路集的路长进行排序,按最小路集的不同路长分两种方法不交化:对于长度为n-1的最小路集,在保持原有弧不变外,将网络图中其余未包含在该条最小路内的弧取逆加入,直接获得不交化运算结果;其余最小路集采用BDD方法进行不交化。最后的实例计算表明,改进的算法有较小的分枝树、较高的计算效率和精度,为大型网络系统的可靠性分析提供了一种新的途径。     

基于二元决策图的网络可靠性评估

提出一种改进-二元决策图(BDD)的网络可靠性评估方法.为了解决BDD构造中有效识别同构子图的问题,将边收缩/删除法应用于BDD的图分解中,并提出了BDD的宽度优先搜索算法,通过遍历BDD图对边进行排序,为布尔函数的不交化提供了一种新的高效途径.实验结果表明,该算法具有精确性高、时间复杂度低的优点,可以避免常规最小路算...     

软件定义车联网的数据转发策略和路由选择技术

针对目前车联网（VANET）数据转发效率低的问题,提出了软件定义网络（SDN）的数据转发策略和路由选择技术。首先,采用了软件定义车联网的分层控制结构,由局部控制器和全局控制器组成,实现数据转发和控制分离,可灵活控制数据转发的方向;然后,设计了单条路段的车辆路由机制,该机制预测车辆节点位置并采用贪心策略,实现数据的稳定传输;其次,设计了多个需求间的路段路由机制,该机制采用广度优先搜索（BFS）算法和边集相结合的方式,实现多个需求间路径不相交,缓解带宽瓶颈问题;最后,通过仿真验证,对比无线自组网按需平面距离向量（AODV）路由,所提出的数据转发策略和路由选择算法在数据分组接收率上提高40%以上,平均延迟时间降低60%左右。实验结果表明,软件定义车联网的数据转发策略和路由选择技术能够提高数据转发效率,减少平均收包延时。