A parallel shortest path algorithm

A new algorithm to find the shortest path between a pair of nodes is presented. In one hand this algorithm expands the search from origin and destination simultaneously, on the other hand it uses a lower bound for the shortest path to guide this search. Computational comparisons with existing algorithms show its efficiency. The implementation on a parallel computer is also discussed.     

A distributed shortest path algorithm for a planar network

An algorithm is presented for finding a single source shortest path tree in a planar undirected distributed network with nonnegative edge costs. The number of messages used by the algorithm is O(n^5/3) on an n-node network. Distributed algorithms are also presented for finding a breath-first spanning tree in general network, for finding a shortest path tree in a general network, for finding a separator of a planar network, and for finding a division of a planar network.     

A new parallel algorithm for constructing Voronoi tessellations from distributed input data

We present a new parallel algorithm for generating consistent Voronoi diagrams from distributed input data for the purposes of simulation and visualization. The algorithm functions by building upon any serial Voronoi tessellation algorithm. The output of such a serial tessellator is used to determine the connectivity of the distributed domains without any assumptions about how points are distributed across those domains, and then in turn to build the portion of the global tessellation local to each domain using information from that domains neighbors. The result is a generalized methodology for adding distributed capabilities to serial tessellation packages. Results from several two-dimensional tests are presented, including strong and weak scaling of its current implementation.     

基于云计算的混合并行遗传算法求解最短路径

为提高最短路径求解问题的效率,提出一种基于云计算的细粒度混合并行遗传算法求解最短路径的方法。方法采用云计算中H adoop的Map Reduce并行编程模型,提高编码效率,同时将细粒度并行遗传算法和禁忌搜索算法结合,提高了寻优算法的计算速度和局部寻优能力,进而提高最短路径的求解效率。仿真结果表明,该方法在计算速度和性能上优于经典遗传算法和并行遗传算法,是一种有效的最短路径求解方法。     

A neural networks algorithm for data path synthesis

This paper presents a deterministic parallel algorithm to solve the data path allocation problem in high-level synthesis. The algorithm is driven by a motion equation that determines the neurons firing conditions based on the modified Hopfield neural network model of computation. The method formulates the allocation problem using the clique partitioning problem, an NP-complete problem, and handles multicycle functional units as well as structural pipelining. The algorithm has a running time complexity of O(1) for a circuit with n operations and c shared resources. A sequential simulator was implemented on a Linux Pentium PC under X-Windows. Several benchmark examples have been implemented and favorable design comparisons to other synthesis systems are reported.     

A shortest path routing algorithm for incomplete WK-recursivenetworks

The WK-recursive networks own two structural advantages: expansibility and equal degree. A network is expansible if no changes to node configuration and link connection are necessary when it is expanded, and of equal degree if its nodes have the same degree no matter what the size is. However, the number of nodes contained in a WK-recursive network is restricted to d^t, where d>1 is the size of the basic building block and t⩾1 is the level of expansion. The incomplete WK-recursive networks, which were proposed to relieve this restriction, are allowed to contain an arbitrary number of basic building blocks, while presenting the advantages of the WK-recursive networks. Designing shortest-path routing algorithms for incomplete networks is in general more difficult than for complete networks. The reason is that most incomplete networks lack a unified representation. One of the contributions of this paper is to demonstrate a useful representation, i.e., the multistage graph representation, for the incomplete WK-recursive networks. On the basis of it, a shortest-path routing algorithm is then proposed. With O(d·t) time preprocessing, this algorithm takes O(t) time for each intermediate node to determine the next node along the shortest path     

求解最短路径的遗传算法中若干问题的讨论

针对道路交通网络中的最短路径问题,讨论了遗传算法中遗传算子的设计及运行参数的选择,提出一种新的交叉算子,提高了种群多样性.通过计算机仿真实验,比较了多种遗传算子设计方案的优劣及不同运行参数对算法效果的影响,为实际应用提供了参考.采用VC语言实现该遗传算法,并应用于实际的电子地图中,结果表明了算法的有效性和实用性.     

Neural networks for shortest path computation and routing incomputer networks

The application of neural networks to the optimum routing problem in packet-switched computer networks, where the goal is to minimize the network-wide average time delay, is addressed. Under appropriate assumptions, the optimum routing algorithm relies heavily on shortest path computations that have to be carried out in real time. For this purpose an efficient neural network shortest path algorithm that is an improved version of previously suggested Hopfield models is proposed. The general principles involved in the design of the proposed neural network are discussed in detail. Its computational power is demonstrated through computer simulations. One of the main features of the proposed model is that it will enable the routing algorithm to be implemented in real time and also to be adaptive to changes in link costs and network topology.     

Finding the shortest path in stochastic networks

Common network parameters, such as number of nodes and arc lengths are frequently subjected to ambiguity as a result of probability law. A number of authors have discussed the calculation of the shortest path in networks with random variable arc lengths. Generally, only a subset of intermediate nodes chosen in accordance with a given probability law can be used to transition from source node to sink node. The determination of a priori path of the minimal length in an incomplete network is defined as a probabilistic shortest path problem. When arc lengths between nodes are randomly assigned variables in an incomplete network the resulting network is known as an incomplete stochastic network. In this paper, the computation of minimal length in incomplete stochastic networks, when travel times between nodes are allowed to be exponentially distributed random variables, is formulated as a linear programming problem. A practical application of the methodology is demonstrated and the results and process compared to the Kulkarni’s [V.G. Kulkarni, Shortest paths in networks with exponentially distributed arc lengths, Networks 16 (1986) 255–274] method.     

一种求受顶点数限制的最短路径的新算法

提出了一种基于逆邻接表求受顶点数限制的最短路径的新算法,其时间复杂度为O(m-2)^*w)(m是受限制的顶点数,w是有向图中弧的条数),优于同类算法。采用逆邻接表作为图的存储结构,该算法很容易实现。     

Hybrid shortest path algorithm for vehicle navigation

Vehicle navigation is one of the important applications of the single-source single-target shortest path algorithm. This application frequently involves large scale networks with limited computing power and memory space. In this study, several heuristic concepts, including hierarchical, bidirectional, and A^*, are combined and used to develop hybrid algorithms that reduce searching space, improve searching speed, and provide the shortest path that closely resembles the behavior of most road users. The proposed algorithms are demonstrated on a real network consisting 374,520 nodes and 502,485 links. The network is preprocessed and separated into two connected subnetworks. The upper layer of network is constructed with high mobility links, while the lower layer comprises high accessibility links. The proposed hybrid algorithms are implemented on both PC and hand-held platforms. Experiments show a significant acceleration compared to the Dijkstra and A^* algorithm. Memory consumption of the hybrid algorithm is also considerably less than traditional algorithms. Results of this study showed the hybrid algorithms have an advantage over the traditional algorithm for vehicle navigation systems.     

最短路径在分布式资源目录中的应用

为了实现对海量多源数据资源的统一管理和资源共享,满足分布数据存储环境下对数据的统一管理和高效检索要求;利用LDAP (lightweight directory access protocol)轻量级目录服务访问协议进行设计并实现了分布式资源目录系统结构,并在此基础上针对服务器间查询速率低的问题提出了应用最短路径算法提高查询速率的方法.实验结果表明,利用LDAP协议实现了分布式资源目录的存储及管理,应用最短路径算法能够显著提高分布式资源目录的查询效率.     

Solving fuzzy shortest path problems by neural networks

In this paper, we introduce the neural networks for solving fuzzy shortest path problems. The penalization of the neural networks is realized after transforming into crisp shortest path model. The procedure and efficiency of this approach are shown with numerical simulations.     

A shortest augmenting path algorithm for dense and sparse linear assignment problems

We develop a shortest augmenting path algorithm for the linear assignment problem. It contains new initialization routines and a special implementation of Dijkstra's shortest path method. For both dense and sparse problems computational experiments show this algorithm to be uniformly faster than the best algorithms from the literature. A Pascal implementation is presented.     

闭合螺线阵列的最短路径算法

介绍了闭合螺线阵列的概念;利用动态规划法中的Floyd算法思想对求解闭合螺线阵列最短路径的问题进行了描述,并给出了具体算法;给出了利用二维数组算法求解闭合螺线阵列最短路径的过程.对于以上两种算法的优缺点进行了比较.这两种算法可以用于解决大多数路径问题.     

Some experience in distributed programming using shortest path spanning trees

In this paper we consider a class of distributed algorithms. Algorithms in this class consist of processes that communicate using a broadcast. We show that only some local information suffices to implement such an algorithm on an arbitrary network. For a number of implementations a theoretical time complexity is derived and some experimental results are given.     

Fast massively parallel algorithms for shortest path within planar figures

s and t within a given planar figure F is considered. The approach contains basic methodology developed for any parallel or distributed system. The 2D scene or the edge of F are represented in the n Cartesian coordinate system (n-CCS). Several algorithms for the shortest path are given, each one to be applied in specified circumstances depending on the exact machine model or on additional information concerning geometrical properties of the figure. If these algorithms are implemented in a parallel depth search machine (PDSM), then the shortest path can be computed in time O(1). The maximum number of processors used is 0(n). The given methodology can also be adapted for producing an approximate solution when the shortest path is approximated by polygonal lines.     

A cost matrix agent for shortest path routing in ad hoc networks

Wireless ad hoc networks do not rely on an existing infrastructure. They are organized as a network with nodes that act as hosts and routers to treat packets. With their frequent changes in topology, ad hoc networks do not rely on the same routing methods as for pre-established wired networks; they require routing methods for mobile wireless networks. To select a path from a source to a destination in dynamic ad hoc networks, an efficient and reliable routing method is very important. In this paper, we introduce a cost-matrix-based routing algorithm. An agent node creates topology information in the form of the adjacency-cost matrix which shows link costs of the network.Based on the adjacency-cost matrix, the minimum-cost matrix and the next-node matrices can be calculated. Based on the minimum-cost matrix and the next-node matrices, the minimum cost between source and destination nodes and between intermediate nodes on the minimum-cost paths can be calculated.The matrices are periodically distributed by the agent to the other nodes. Based on the minimum-cost matrix and the next-node matrices, each node decides the minimum-cost path to its destination. Because none of the nodes except the agent needs to gather network topology information, the control overhead of the proposed method is small compared with those of the general table-driven routing protocols.     

A genetic algorithm for shortest path routing problem and the sizing of populations

This paper presents a genetic algorithmic approach to the shortest path (SP) routing problem. Variable-length chromosomes (strings) and their genes (parameters) have been used for encoding the problem. The crossover operation exchanges partial chromosomes (partial routes) at positionally independent crossing sites and the mutation operation maintains the genetic diversity of the population. The proposed algorithm can cure all the infeasible chromosomes with a simple repair function. Crossover and mutation together provide a search capability that results in improved quality of solution and enhanced rate of convergence. This paper also develops a population-sizing equation that facilitates a solution with desired quality. It is based on the gambler ruin model; the equation has been further enhanced and generalized. The equation relates the size of the population, quality of solution, cardinality of the alphabet, and other parameters of the proposed algorithm. Computer simulations show that the proposed algorithm exhibits a much better quality of solution (route optimality) and a much higher rate of convergence than other algorithms. The results are relatively independent of problem types for almost all source-destination pairs. Furthermore, simulation studies emphasize the usefulness of the population-sizing equation. The equation scales to larger networks. It is felt that it can be used for determining an adequate population size in the SP routing problem.