基于交通网络最短路径搜索的改进算法

对全源最短路径搜索算法进行了深入的研究分析,并结合国内城市道路交通的实际情况,提出了基于边序列最短路径搜索算法的一种改进算法——EBSP*算法。该算法在平均时间复杂度上比传统的Floyd最短路径搜索算法有较大的提高。     

基于遗传算法的最短路径的计算

交通系统中的最优路径算法等同于图论中的最短路径算法,根据不同的具体要求可以是长度最短或行驶时间最短。由于问题的特征、网络特性等的纷繁复杂最短路径算法表现出多样性。除了经典的方法外,近年来出现的模拟退火、Tabu搜索和遗传算法等在优化问题中获得了广泛的应用,本文主要讨论了用改进的遗传算法求解最短路径的方法。     

BGrR:基于粒计算的大规模网络路径的加速方法

大规模网络路径问题是社会网络信息处理的基本问题。将粒计算方法引入到大规模网络研究中,结合社会网络分层和社团结构性质建立网络的多粒度层次模型,实现网络的多粒度存储,将大规模网络复杂结构映射到不同粒度空间中。为了降低问题求解的复杂度,将最短路径问题映射到不同粒度空间中,将搜索过程从粗粒度空间向细粒度空间跳转以搜索路径信息,提出基于多粒度空间的最短路径搜索算法(BGrR)来加速大规模网络路径搜索。在实验中,以城市道路交通网络为数据源,通过与A*和ALT方法比较,验证了所提算法的有效性。     

复杂网络中最短路径问题的求解算法研究

本文以时间代价作为目标函数,针对复杂网络的优化问题进行研究,给出了目标评价函数模型的建立过程,提出了改进的A＊算法求解复杂网络中最短路径问题的算法,并以城市交通为例,对算法进行了验证,实验结果表明所提出的算法可适用于一般多重图中最短路径问题的快速求解,具有广泛的应用价值。     

VC环境下遗传算法在网络最短路径优化中的设计与实现

针对网络最短路径的有效智能求解,设计了智能算法——遗传算法在基于Visual C++6.0平台下对网络最短路径问题的实现方案,阐明了遗传算法在求解网络最短路径问题中包括编码、种群生成和遗传算子的具体步骤。通过实验,验证了设计方法的可行性和有效性,同时,该方法具有一定的理论意义和现实价值。     

时间依赖的网络中最小时间路径算法

时间依赖的网络与传统网络模型相比更具有现实意义，具有广泛的应用领域，交通网络和通信网络可以抽象为时间依赖的网络模型，当模型中弧的工度是时间依赖的变量，最短路径问题的求解变得非常困难，早期的研究者通过具体的网络实例认识到传统最短路径算法在这种情况下是不正确的，因此给出限制性条件使得传统最短路径算法是有效的。该文从最短路径算法的理论基础入手，从理论上证明了传统最短路径算法，如Dijkstra算法和标号设置算法，在时间依赖的网络上不能有效地求解最短路径问题，并且，在没有任何限制性条件下，给出了时间依赖的网络模型，理论基础，求解最小时间路径的优化条件和SPTDN算法，从理论上证明了SPTDN算法的正确性，算法的实验结果是正确的，最后给出了时间依赖的网络应用实例。     

基于最短路径查询的城市公交网络拓扑建模研究

最短路径分析是地理信息系统（GIS）中网络分析的一项重要功能，等价于图论中的节点间求解最短路径问题。对地理网络进行地理分析和建模，以实现最短路径搜索已经有大量论文讨论。但是专门针对城市公交网络的建模和路径寻优，则少鲜有研究，而且已有的一些网络模型也不能直接应用到公交网络寻优中，本文应用图论理论，讨论公共交通网络的拓扑建模，实现公交网络最优路径的查询。     

基于遗传算法的动态网络中最短路径问题算法

提出了一种以随机Dijkstra最短路径算法为基础,运用遗传算法来求解动态路径诱导系统 中最短路径问题(ShortestPathproblemonDynamicRouteGuidanceSystem,SPDRGS)的算法。通过运用 该随机Dijkstra算法解决了将遗传算法应用与最短路径问题中初始种群的产生问题。考虑到目前动态 路径诱导系统(DynamicRouteGuidanceSystem,DRGS)对路径诱导算法的时间复杂度和网络约束条件 的要求,此算法不仅能够较快地求出较优的路径而且对网络没有任何的约束条件,同时对离散和连续的 动态网络模型有效,因此符合DRGS的要求。     

复杂网络中最短K条路径问题的求解算法研究

以时间代价作为目标函数,针对复杂网络的优化问题进行研究,给出了目标评价函数模型的建立过程,提出了基于改进的A*算法求解复杂网络中最短K条路径问题的算法,并以城市交通为例,对算法进行了验证。实验结果表明所提出的算法可适用于一般多重图中最短K条路径问题的快速求解,具有广泛的应用价值。     

大规模定量传输的时间扩展网络K最短路径算法

大规模的军用物资调度,需要传输的物资远远超出保障网络实际传输能力的情况下,现有的Dijkstra算法、Floyd算法以及传统的网络K-最短路径算法,难以求解这类网络调度优化问题。在蚁群算法的基础上,设计了一种基于时间扩展的网络K-最短路径算法,满足网络传输一致性假设的前提下,求解大规模定量传输问题。最后给出面向任务的物流保障网络调度的应用实例,获得满意的网络调度优化方案。     

A Slightly Improved Sub-Cubic Algorithm for the All Pairs Shortest Paths Problem with Real Edge Lengths

We present an -time algorithm for the All Pairs Shortest Paths (APSP) problem for directed graphs with real edge lengths. This slightly improves previous algorithms for the problem obtained by Fredman, Dobosiewicz, Han, and Takaoka.     

低代价最短路径树的快速算法

低代价最短路径树是一种广泛使用的多播树.它能够在保证传送时延最小的同时尽量降低带宽消耗.在DDSP(destination-driven shortest path)算法的基础上,通过改进节点的搜索过程,提出了快速低代价最短路径树算法FLSPT(fast loW-coSt shortest path tree).该算法构造的最短路径树与DDSP算法构造的树具有相同的性能,但其时间复杂度低于DDSP算法.随机网络模型的仿真结果表明,FLSPT算法效率更高.     

SPLZ: An efficient algorithm for single source shortest path problem using compression method

Efficient solution of the single source shortest path (SSSP) problem on road networks is an important requirement for numerous real-world applications. This paper introduces an algorithm for the SSSP problem using compression method. Owning to precomputing and storing all-pairs shortest path (APSP), the process of solving SSSP problem is a simple lookup of a little data from precomputed APSP and decompression. APSP without compression needs at least 1TB memory for a road network with one million vertices. Our algorithm can compress such an APSP into several GB, and ensure a good performance of decompression. In our experiment on a dataset about Northwest USA (with 1.2 millions vertices), our method can achieve about three orders of magnitude faster than Dijkstra algorithm based on binary heap.     

Graphical processing unit‐based parallelization of the Open Shortest Path First and Border Gateway Protocol routing protocols

Exponentially growing number of devices on Internet incurs an ever‐increasing load on the network routers in executing network protocols. Parallel processing has recently become an unavoidable means to scale up the router performance. The research effort elaborated in this paper is focused on exploiting the modern trends of general‐purpose computing on graphics processing unit computing in speeding up the execution of network protocols. An additional benefit is off‐loading the CPU, which can now be fully dedicated to the packet processing and forwarding. To this end, the Shortest Path First algorithm in the Open Shortest Path First protocol and the choice of the best routes in the Border Gateway Protocol are parallelized for efficient execution on Compute Unified Device Architecture platform. An evaluation study was conducted on three different graphics processing units with representative network workload for a varying number of routes and devices. The obtained speedup results confirmed the viability and cost‐effectiveness of such an approach. Copyright © 2014 John Wiley & Sons, Ltd.     

无线传感器网络数据融合路由算法的改进

无线传感器网络能量有限,数据融合能通过合并冗余数据减少传输数据量,但其本身的代价不可忽略。针对该问题,研究数据融合代价和数据传输代价对数据融合路由的影响,在基于决策数据融合技术AFST中,对直传数据采用动态最短路径(DSPT)算法,动态识别网络环境和数据特征变化,以最小的代价调整路由。实验与分析结果表明,当网络结构发生变化时,DSPT算法比SPT算法效率更高、更节能。     

Layered data association using graph-theoretic formulation with applications to tennis ball tracking in monocular sequences

In this paper, we propose a multi-layered data association scheme with graph-theoretic formulation for tracking multiple objects that undergo switching dynamics in clutter. The proposed scheme takes as input object candidates detected in each frame. At the object candidate level, "tracklets' are "grown' from sets of candidates that have high probabilities of containing only true positives. At the tracklet level, a directed and weighted graph is constructed, where each node is a tracklet, and the edge weight between two nodes is defined according to the "compatibility' of the two tracklets. The association problem is then formulated as an all-pairs shortest path (APSP) problem in this graph. Finally, at the path level, by analyzing the all-pairs shortest paths, all object trajectories are identified, and track initiation and track termination are automatically dealt with. By exploiting a special topological property of the graph, we have also developed a more efficient APSP algorithm than the general-purpose ones. The proposed data association scheme is applied to tennis sequences to track tennis balls. Experiments show that it works well on sequences where other data association methods perform poorly or fail completely.     

Optimizing an APSP implementation for NVIDIA GPUs using kernel characterization criteria

During the last years, GPU manycore devices have demonstrated their usefulness to accelerate computationally intensive problems. Although arriving at a parallelization of a highly parallel algorithm is an affordable task, the optimization of GPU codes is a challenging activity. The main reason for this is the number of parameters, programming choices, and tuning techniques available, many of them related with complex and sometimes hidden architecture details. A useful strategy to systematically attack these optimization problems is to characterize the different kernels of the application, and use this knowledge to select appropriate configuration parameters. The All-Pair Shortest-Path (APSP) problem is a well-known problem in graph theory whose objective is to find the shortest paths between any pairs of nodes in a graph. This problem can be solved by highly parallel and computational intensive tasks, being a good candidate to be exploited by manycore devices. In this paper, we use kernel characterization criteria to optimize an APSP algorithm implementation for NVIDIA GPUs. Our experimental results show that the combined use of proper configuration policies, and the concurrent kernels capability of new CUDA architectures, leads to a performance improvement of up to 62 % with respect to one of the possible configurations recommended by CUDA, considered as baseline.     

社会网络分析和社团信息挖掘的三项探索——挖掘虚拟社团的结构、核心和通信行为

社会网络分析是数据挖掘的新热点。文中综述了社会网络分析研究动态，介绍了作者近期在社会网络挖掘方面的三项探索，包括:（a）虚拟社团的结构挖掘，讨论了初始社团生成、权重计算，社团树生成和社团结构挖掘算法。(b) 基于六度分割和最短路径社团核心成员挖掘，讨论了计算节点间的最短路径，根据六度分割定理的剪枝，基于SPLINE算法和中心度挖掘犯罪子团伙中的核心的技术。(c)基于用户属性的通信行为挖掘，讨论了用户个性和通信行为关系的量化描述，采用911事件解密数据来建立社会网络，用于模拟恐怖分子间邮件的收发。     

Reduction approaches for robust shortest path problems

We investigate the uncertain versions of two classical combinatorial optimization problems, namely the Single-Pair Shortest Path Problem (SP-SPP) and the Single-Source Shortest Path Problem (SS-SPP). The former consists of finding a path of minimum length connecting two specific nodes in a finite directed graph G; the latter consists of finding the shortest paths from a fixed node to the remaining nodes of G. When considering the uncertain versions of both problems we assume that cycles may occur in G and that arc lengths are (possibly degenerating) nonnegative intervals. We provide sufficient conditions for a node and an arc to be always or never in an optimal solution of the Minimax regret Single-Pair Shortest Path Problem (MSP-SPP). Similarly, we provide sufficient conditions for an arc to be always or never in an optimal solution of the Minimax regret Single-Source Shortest Path Problem (MSS-SPP). We exploit such results to develop pegging tests useful to reduce the overall running time necessary to exactly solve both problems.     

最短路由问题的改进单亲进化遗传算法

基于信息素动态更新的蚁群算法(DACO)求解大规模最短路由问题收敛时间过长,单亲进化遗传算法(PEGA)在产生初始种群、选择父体及基因换位等操作中存在随机性太大的问题,论章将这两种算法相结合,提出了基于改进蚁群算法的单亲进化遗传算法(DACO-PEGA),该算法通过控制蚁群周游次数,求得满意可行解或次优解,再将已得路由作为初始种群进行优化改良,求得最短路由。实验结果表明,该算法应用于求解最短路由问题行之有效.