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1

Kumar, Praveen, and Anil Kumar Singh. "MapReduce Algorithm for Single Source Shortest Path Problem." International Journal of Computer Network and Information Security 12, no. 3 (2020): 11–21. http://dx.doi.org/10.5815/ijcnis.2020.03.02.

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2

Carlisle, Homer, Albert Crawford, and Sallie Sheppard. "ADA multitasking and the single source shortest path problem." Parallel Computing 4, no. 1 (1987): 75–91. http://dx.doi.org/10.1016/0167-8191(87)90064-0.

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3

Lou, Yuan Sheng, Wen Yuan Zhang, Feng Xu, Yu Wang, and Sheng Chen. "Parallel Implementation of Single-Source Shortest Path Algorithm Based on Haloop." Applied Mechanics and Materials 220-223 (November 2012): 2428–32. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2428.

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As a typical problem of graph theory, Single-Source Shortest Path(SSSP) has a wide range of applications and research. The traditional MapReduce framework such as Hadoop has been applied to SSSP problem. However, in this way, it may need writing and reading the disk frequently, transmitting data largely, beacuse of SSSP’s iterative. Haloop is a parallel programming framework which makes an improvement based on Hadoop framework, and adapts itself to iterative programming. Hence, this article represents the SSSP problem with an iterative way firstly, and then we put forward the implementation me
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4

Lei, Guoqing, Yong Dou, Rongchun Li, and Fei Xia. "An FPGA Implementation for Solving the Large Single-Source-Shortest-Path Problem." IEEE Transactions on Circuits and Systems II: Express Briefs 63, no. 5 (2016): 473–77. http://dx.doi.org/10.1109/tcsii.2015.2505998.

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5

Bhowmik, B., and S. Nag Chowdhury. "Prograph Based Analysis of Single Source Shortest Path Problem with Few Distinct Positive Lengths." Engineering, Technology & Applied Science Research 1, no. 4 (2011): 90–97. http://dx.doi.org/10.48084/etasr.41.

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In this paper we propose an experimental study model S3P2 of a fast fully dynamic programming algorithm design technique in finite directed graphs with few distinct nonnegative real edge weights. The Bellman-Ford’s approach for shortest path problems has come out in various implementations. In this paper the approach once again is re-investigated with adjacency matrix selection in associate least running time. The model tests proposed algorithm against arbitrarily but positive valued weighted digraphs introducing notion of Prograph that speeds up finding the shortest path over previous impleme
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6

Zhang, Bingwu, Xiucui Guan, Chunyuan He, and Shuguo Wang. "Algorithms for the Shortest Path Improvement Problems under Unit Hamming Distance." Journal of Applied Mathematics 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/847317.

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In a shortest path improvement problem under unit Hamming distance (denoted by SPIUH), an edge weighted graph with a set of source-terminal pairs is given; we need to modify the lengths of edges by a minimum cost under unit Hamming distance such that the modified distances of the shortest paths are upper bounded by given values. The SPIUH problem on arborescent network is formulated as a 0-1 integer programming model. Some strongly polynomial time algorithms are designed for the problems on some special arborescent networks. Firstly, two greedy algorithms are proposed for problems on chain net
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7

COSMA, OVIDIU, PETRICA C. POP, and IOANA ZELINA. "A novel genetic algorithm for solving the clustered shortest-path tree problem." Carpathian Journal of Mathematics 36, no. 3 (2020): 401–14. http://dx.doi.org/10.37193/cjm.2020.03.08.

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The clustered shortest-path tree problem is an extension of the classical single-source shortest-path problem, in which, given a graph with the set of nodes divided into a redefined, mutually exclusive and exhaustive set of clusters, we want to determine a shortest-path spanning tree from a given source to all the other nodes of the graph, with the property that each cluster should induce a connected subtree. The investigated problem proved to be NP-hard and therefore we proposed an efficient genetic algorithm in order to solve it. The preliminary computational results reported on a set of ben
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8

Sun, Jingwei, and Guangzhong Sun. "SPLZ: An efficient algorithm for single source shortest path problem using compression method." GeoInformatica 20, no. 1 (2015): 1–18. http://dx.doi.org/10.1007/s10707-015-0229-7.

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9

Raman, Rajeev. "Recent results on the single-source shortest paths problem." ACM SIGACT News 28, no. 2 (1997): 81–87. http://dx.doi.org/10.1145/261342.261352.

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10

Cui, Huanqing, Ruixue Liu, Shaohua Xu, and Chuanai Zhou. "DMGA: A Distributed Shortest Path Algorithm for Multistage Graph." Scientific Programming 2021 (May 31, 2021): 1–14. http://dx.doi.org/10.1155/2021/6639008.

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The multistage graph problem is a special kind of single-source single-sink shortest path problem. It is difficult even impossible to solve the large-scale multistage graphs using a single machine with sequential algorithms. There are many distributed graph computing systems that can solve this problem, but they are often designed for general large-scale graphs, which do not consider the special characteristics of multistage graphs. This paper proposes DMGA (Distributed Multistage Graph Algorithm) to solve the shortest path problem according to the structural characteristics of multistage grap
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11

Alam, Md Almash, and Md Omar Faruq. "Finding Shortest Path for Road Network Using Dijkstra’s Algorithm." Bangladesh Journal of Multidisciplinary Scientific Research 1, no. 2 (2019): 41–45. http://dx.doi.org/10.46281/bjmsr.v1i2.366.

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Roads play a Major role to the people live in various states, cities, town and villages, from each and every day they travel to work, to schools, to business meetings, and to transport their goods. Even in this modern era whole world used roads, remain one of the most useful mediums used most frequently for transportation and travel. The manipulation of shortest paths between various locations appears to be a major problem in the road networks. The large range of applications and product was introduced to solve or overcome the difficulties by developing different shortest path algorithms. Even
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12

Doerr, Benjamin, Edda Happ, and Christian Klein. "Tight Analysis of the (1+1)-EA for the Single Source Shortest Path Problem." Evolutionary Computation 19, no. 4 (2011): 673–91. http://dx.doi.org/10.1162/evco_a_00047.

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We conduct a rigorous analysis of the (1+1) evolutionary algorithm for the single source shortest path problem proposed by Scharnow, Tinnefeld, and Wegener (The analyses of evolutionary algorithms on sorting and shortest paths problems, 2004, Journal of Mathematical Modelling and Algorithms, 3(4):349–366). We prove that with high probability, the optimization time is O(n2 max{ℓ, log(n)}), where ℓ is the smallest integer such that any vertex can be reached from the source via a shortest path having at most ℓ edges. This bound is tight. For all values of n and ℓ we provide a graph with edge weig
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13

Zhou, Quan, Hui Zhao, Huijie Zhang, Shulin Tian, and Zhen Liu. "EFS: Entropy First Search for Dynamic Shortest Path Problem in Large Sparse Graphs." Journal of Computational and Theoretical Nanoscience 14, no. 1 (2017): 367–83. http://dx.doi.org/10.1166/jctn.2017.6330.

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Searching the dynamic shortest path is a hot topic recently. In this paper we proposed a new method to solve the dynamic single source shortest paths (SSSP) problem in sparse graphs. The main contributions are three: firstly, in the preprocessing stage, we use the unreachable and unstartable characteristics to avoid most of the non-solution path search. Secondly, Entropy first search (EFS) is introduced to speed up the search process in finding the possible shortest path and then the algorithm can converges as soon as possible. In addition, the update algorithm for dynamic shortest path search
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14

Nigam, Nilanshi, Vivek Bhushan, and Abdul Muttalib. "Comparative Study for Trends of Solving Single Source Shortest Path Problems." International Journal of Computer Applications 138, no. 5 (2016): 31–35. http://dx.doi.org/10.5120/ijca2016908832.

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15

Asano, Yasuhito, and Hiroshi Imai. "PRACTICAL EFFICIENCY OF THE LINEAR-TIME ALGORITHM FOR THE SINGLE SOURCE SHORTEST PATH PROBLEM." Journal of the Operations Research Society of Japan 43, no. 4 (2000): 431–47. http://dx.doi.org/10.15807/jorsj.43.431.

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16

Träff, Jesper L., and Christos D. Zaroliagis. "A Simple Parallel Algorithm for the Single-Source Shortest Path Problem on Planar Digraphs." Journal of Parallel and Distributed Computing 60, no. 9 (2000): 1103–24. http://dx.doi.org/10.1006/jpdc.2000.1646.

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17

Shi, Hanmao, and Thomas H. Spencer. "Time–Work Tradeoffs of the Single-Source Shortest Paths Problem." Journal of Algorithms 30, no. 1 (1999): 19–32. http://dx.doi.org/10.1006/jagm.1998.0968.

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18

Alani, Sameer, Atheer Baseel, Mustafa Maad Hamdi, and Sami Abduljabbar Rashid. "A hybrid technique for single-source shortest path-based on A* algorithm and ant colony optimization." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 2 (2020): 356. http://dx.doi.org/10.11591/ijai.v9.i2.pp356-363.

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<span lang="EN-US">In the single-source shortest path (SSSP) problem, the shortest paths from a source vertex v to all other vertices in a graph should be executed in the best way. A common algorithm to solve the (SSSP) is the A* and Ant colony optimization (ACO). However, the traditional A* is fast but not accurate because it doesn’t calculate all node's distance of the graph. Moreover, it is slow in path computation. In this paper, we propose a new technique that consists of a hybridizing of A* algorithm and ant colony optimization (ACO). This solution depends on applying the optimizat
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19

Zhou, Xin. "An Improved SPFA Algorithm for Single-Source Shortest Path Problem Using Forward Star Data Structure." International Journal of Managing Information Technology 6, no. 1 (2014): 15–21. http://dx.doi.org/10.5121/ijmit.2014.6402.

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20

Orlin, James B., Kamesh Madduri, K. Subramani, and M. Williamson. "A faster algorithm for the single source shortest path problem with few distinct positive lengths." Journal of Discrete Algorithms 8, no. 2 (2010): 189–98. http://dx.doi.org/10.1016/j.jda.2009.03.001.

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21

Gualà, L., and G. Proietti. "Efficient truthful mechanisms for the single-source shortest paths tree problem." Concurrency and Computation: Practice and Experience 19, no. 17 (2007): 2285–97. http://dx.doi.org/10.1002/cpe.1167.

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22

Tamimi, Abdelfatah Aref. "Comparison Studies for Different Shortest path Algorithms." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 14, no. 8 (2015): 5979–86. http://dx.doi.org/10.24297/ijct.v14i8.1857.

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While technological revolution has active role to the increase of computer information, growing computational capabilities of devices, and raise the level of knowledge abilities, and skills. Increase developments in science and technology. In graph used the shortest path algorithms for solving the shortest path problem. The shortest path can be single pair shortest path problem or all pairs shortest path problem. This paper discuss briefly the shortest path algorithms such as Dijkstra's algorithm, Bellman-Ford algorithm,Floyd- Warshall algorithm, and johnson's algorithm. It describes the previ
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23

Wang, Charlie C. L., and Kai Tang. "Optimal Boundary Triangulations of an Interpolating Ruled Surface." Journal of Computing and Information Science in Engineering 5, no. 4 (2005): 291–301. http://dx.doi.org/10.1115/1.2052850.

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We investigate how to define a triangulated ruled surface interpolating two polygonal directrices that will meet a variety of optimization objectives which originate from many CAD/CAM and geometric modeling applications. This optimal triangulation problem is formulated as a combinatorial search problem whose search space however has the size tightly factorial to the numbers of points on the two directrices. To tackle this bound, we introduce a novel computational tool called multilayer directed graph and establish an equivalence between the optimal triangulation and the single-source shortest
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24

Yan, Jinjin, Sisi Zlatanova, Jinwoo (Brian) Lee, and Qingxiang Liu. "Indoor Traveling Salesman Problem (ITSP) Path Planning." ISPRS International Journal of Geo-Information 10, no. 9 (2021): 616. http://dx.doi.org/10.3390/ijgi10090616.

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With the growing complexity of indoor living environments, people have an increasing demand for indoor navigation. Currently, navigation path options in indoor are monotonous as existing navigation systems commonly offer single-source shortest-distance or fastest paths. Such path options might be not always attractive. For instance, pedestrians in a shopping mall may be interested in a path that navigates through multiple places starting from and ending at the same location. Here, we name it as the indoor traveling salesman problem (ITSP) path. As its name implies, this path type is similar to
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25

Prajapati, G. L., Pulkit Singhal, Ayush Ranjan, and Neelesh Chourasia. "An Efficient Scheme for the Single Source Shortest Path Problem based on Dijkstra and SPFA Methodologies." International Journal of Computer Applications 163, no. 8 (2017): 46–52. http://dx.doi.org/10.5120/ijca2017913694.

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26

Zhang, Xiaoge, Qing Wang, Andrew Adamatzky, Felix T. S. Chan, Sankaran Mahadevan, and Yong Deng. "An ImprovedPhysarum polycephalumAlgorithm for the Shortest Path Problem." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/487069.

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Shortest path is among classical problems of computer science. The problems are solved by hundreds of algorithms, silicon computing architectures and novel substrate, unconventional, computing devices. Acellular slime mouldP. polycephalumis originally famous as a computing biological substrate due to its alleged ability to approximate shortest path from its inoculation site to a source of nutrients. Several algorithms were designed based on properties of the slime mould. Many of thePhysarum-inspired algorithms suffer from a low converge speed. To accelerate the search of a solution and reduce
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27

Zhang, Shuijian, Xuejun Liu, and Meizhen Wang. "A novel ant colony optimization algorithm for the shortest-path problem in traffic networks." Filomat 32, no. 5 (2018): 1619–28. http://dx.doi.org/10.2298/fil1805619z.

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The Ant Colony Optimization (ACO) algorithm is a metaheuristic nature-inspired technique for solving various combinatorial optimization problems. The shortest-path problem is an important combinatorial optimization problem in network optimization. In this paper, a novel algorithm based on ACO to solve the single-pair shortest-path problem in traffic networks is introduced. In this algorithm, a new strategy is developed to find the best solution in a local search, by which the ants seek the shortest path using both a pheromone-trail-following mechanism and an orientation-guidance mechanism. A n
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28

Frigioni, Daniele, Mario Ioffreda, Umberto Nanni, and Giulio Pasqualone. "Experimental analysis of dynamic algorithms for the single source shortest paths problem." ACM Journal of Experimental Algorithmics 3 (September 1998): 5. http://dx.doi.org/10.1145/297096.297147.

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29

Yang, Lehua, Dongmei Li, and Ruipu Tan. "Particle swarm optimization for the shortest path problem." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 1353–73. http://dx.doi.org/10.3233/jifs-210233.

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Solving the shortest path problem is very difficult in situations such as emergency rescue after a typhoon: road-damage caused by a typhoon causes the weight of the rescue path to be uncertain and impossible to represent using single, precise numbers. In such uncertain environments, neutrosophic numbers can express the edge distance more effectively: membership in a neutrosophic set has different degrees of truth, indeterminacy, and falsity. This paper proposes a shortest path solution method for interval-valued neutrosophic graphs using the particle swarm optimization algorithm. Furthermore,
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30

ARIFFIN, WAN NOR MUNIRAH, SHAZALINA MAT ZIN, and SHAHARUDDIN SALLEH. "SHORTEST PATH TECHNIQUE FOR SWITCHING IN A MESH NETWORK." International Journal of Modern Physics: Conference Series 09 (January 2012): 488–94. http://dx.doi.org/10.1142/s2010194512005570.

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Switching is a technique to route data and instructions between pairs of source-destination nodes or among multiple nodes for broadcast communication. We realized that the shortest path problem has a wide application in the design of networks. Therefore, in this paper, we present a mesh network as our switching mechanism for computing the shortest path between the source and destination in our simulation model, developed using C++ on the Windows environment. The Floyd-Warshall algorithm is applied in finding the shortest path in all-pairs nodes.
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31

Subramani, K., and Kamesh Madduri. "Two-level heaps: a new priority queue structure with applications to the single source shortest path problem." Computing 90, no. 3-4 (2010): 113–30. http://dx.doi.org/10.1007/s00607-010-0112-1.

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32

Rezapour, Hassan, and Gholamhassan Shirdel. "The time-varying shortest path problem with fuzzy transit costs and speedup." Acta Universitatis Sapientiae, Mathematica 8, no. 1 (2016): 166–73. http://dx.doi.org/10.1515/ausm-2016-0010.

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Abstract In this paper, we focus on the time-varying shortest path problem, where the transit costs are fuzzy numbers. Moreover, we consider this problem in which the transit time can be shortened at a fuzzy speedup cost. Speedup may also be a better decision to find the shortest path from a source vertex to a specified vertex.
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33

Yella Swamy, K., Saranya Gogineni, Yaswanth Gunturu, Deepchand Gudapati, and Ramu Tirumalasetti. "Finding the shortest path using the ant colony optimization." International Journal of Engineering & Technology 7, no. 1.1 (2017): 392. http://dx.doi.org/10.14419/ijet.v7i1.1.9859.

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An ant colony optimization(ACO) is a techniquewhich is recently introduced ,and it is applied to solve several np-hard problems ,we can get optimal solution in a short time Main concept of the ACO is based on the behavior of ants in their colony for finding a source of food. They will communicate indirectly through pheromone trails. Computer based simulation is can generate good solution by using artificial ants, by using that general behavior we are solving travelling Sale man problem.
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34

Mao, Guoyong, and Ning Zhang. "Analysis of Average Shortest-Path Length of Scale-Free Network." Journal of Applied Mathematics 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/865643.

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Computing the average shortest-path length of a large scale-free network needs much memory space and computation time. Hence, parallel computing must be applied. In order to solve the load-balancing problem for coarse-grained parallelization, the relationship between the computing time of a single-source shortest-path length of node and the features of node is studied. We present a dynamic programming model using the average outdegree of neighboring nodes of different levels as the variable and the minimum time difference as the target. The coefficients are determined on time measurable networ
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35

Takahashi, Natsumi, Tomoaki Akiba, Shuhei Nomura, and Hisashi Yamamoto. "An Approach for the Fast Calculation Method of Pareto Solutions of a Two-objective Network." International Journal of Reliability, Quality and Safety Engineering 22, no. 01 (2015): 1550005. http://dx.doi.org/10.1142/s0218539315500059.

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The shortest path problem is a kind of optimization problem and its aim is to find the shortest path connecting two specific nodes in a network, where each edge has its distance. When considering not only the distances between the nodes but also some other information, the problem is formulated as a multi-objective shortest path problem that involves multiple conflicting objective functions. The multi-objective shortest path problem is a kind of optimization problem of multi-objective network. In the general cases, multi-objectives are rarely optimized by a solution. So, to solve the multi-obj
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36

Behzadi, S., and M. Kolbadinejad. "INTRODUCING A NOVEL METHOD TO SOLVE SHORTEST PATH PROBLEMS BASED ON STRUCTURE OF NETWORK USING GENETIC ALGORITHM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 201–3. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-201-2019.

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Abstract. The shortest path problem is widely applied in transportation, communication and computer networks. It addresses the challenges of determining a path with minimum distance, time or cost from a source to the destination. Network analysis provides strong decision support for users in searching shortest path. A lot of algorithms are designed for solving shortest path but most of them did not considered the conditions of the networks. Genetic Algorithm is a kind of Algorithm that has a lot of efficiency and it can be used for solving many kinds of problems. It also can be used based on t
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37

BRANDES, ULRIK, and CHRISTIAN PICH. "CENTRALITY ESTIMATION IN LARGE NETWORKS." International Journal of Bifurcation and Chaos 17, no. 07 (2007): 2303–18. http://dx.doi.org/10.1142/s0218127407018403.

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Centrality indices are an essential concept in network analysis. For those based on shortest-path distances the computation is at least quadratic in the number of nodes, since it usually involves solving the single-source shortest-paths (SSSP) problem from every node. Therefore, exact computation is infeasible for many large networks of interest today. Centrality scores can be estimated, however, from a limited number of SSSP computations. We present results from an experimental study of the quality of such estimates under various selection strategies for the source vertices.
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38

CHANG, EE-CHIEN, SUNG WOO CHOI, DO YONG KWON, HYUNGJU PARK, and CHEE K. YAP. "SHORTEST PATH AMIDST DISC OBSTACLES IS COMPUTABLE." International Journal of Computational Geometry & Applications 16, no. 05n06 (2006): 567–90. http://dx.doi.org/10.1142/s0218195906002191.

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An open question in Exact Geometric Computation is whether there are transcendental computations that can be made "geometrically exact". Perhaps the simplest such problem in computational geometry is that of computing the shortest obstacle-avoiding path between two points p,q in the plane, where the obstacles are a collection of n discs. This problem can be solved in O(n2 log n) time in the Real RAM model, but nothing was known about its computability in the standard (Turing) model of computation. We first give a direct proof of the Turing-computability of this problem, provided the radii of t
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39

Deepa, G., Priyank Kumar, A. Manimaran, K. Rajakumar, and V. Krishnamoorthy. "Dijkstra Algorithm Application: Shortest Distance between Buildings." International Journal of Engineering & Technology 7, no. 4.10 (2018): 974. http://dx.doi.org/10.14419/ijet.v7i4.10.26638.

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The shortest path algorithm is one of the best choices for implementation of data structures. The shortest path (SP) problem involves the problem of finding a suitable path between “two vertices or nodes in a graph” in such a way that the sum of the weights of its component edges is minimal. There are many theories for solving this problem one of the widely used way solution for solving this problem is Dijkstra’s algorithm (DA) which is also widely used in many engineering calculation works also. There are two types of DA one is the basic one and other one is optimized. This paper is focused o
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40

Saerens, Marco, Youssef Achbany, François Fouss, and Luh Yen. "Randomized Shortest-Path Problems: Two Related Models." Neural Computation 21, no. 8 (2009): 2363–404. http://dx.doi.org/10.1162/neco.2009.11-07-643.

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This letter addresses the problem of designing the transition probabilities of a finite Markov chain (the policy) in order to minimize the expected cost for reaching a destination node from a source node while maintaining a fixed level of entropy spread throughout the network (the exploration). It is motivated by the following scenario. Suppose you have to route agents through a network in some optimal way, for instance, by minimizing the total travel cost—nothing particular up to now—you could use a standard shortest-path algorithm. Suppose, however, that you want to avoid pure deterministic
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41

Liu, Ruxiang. "Study on single-valued neutrosophic graph with application in shortest path problem." CAAI Transactions on Intelligence Technology 5, no. 4 (2020): 308–13. http://dx.doi.org/10.1049/trit.2020.0111.

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42

Gayathri, R. G., and Jyothisha J. Nair. "ex-FTCD: A novel mapreduce model for distributed multi source shortest path problem." Journal of Intelligent & Fuzzy Systems 34, no. 3 (2018): 1643–52. http://dx.doi.org/10.3233/jifs-169458.

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43

Yin, Xiangdong, and Jie Yang. "Shortest Paths Based Web Service Selection in Internet of Things." Journal of Sensors 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/958350.

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The connecting of things to the Internet makes it possible for smart things to access all kinds of Web services. However, smart things are energy-limited, and suitable selection of Web services will consume less resources. In this paper, we study the problem of selecting some Web service from the candidate set. We formulate this selection of Web services for smart things as single-source many-target shortest path problem. We design algorithms based on the Dijkstra and breadth-first search algorithms, propose an efficient pruning algorithm for breadth-first search, and analyze their performance
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44

Vahidipour, S. Mehdi, Mohammad Reza Meybodi, and Mehdi Esnaashari. "Finding the Shortest Path in Stochastic Graphs Using Learning Automata and Adaptive Stochastic Petri Nets." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, no. 03 (2017): 427–55. http://dx.doi.org/10.1142/s0218488517500180.

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Shortest path problem in stochastic graphs has been recently studied in the literature and a number of algorithms has been provided to find it using varieties of learning automata models. However, all these algorithms suffer from two common drawbacks: low speed and lack of a clear termination condition. In this paper, we propose a novel learning automata-based algorithm for this problem which can speed up the process of finding the shortest path using parallelism. For this parallelism, several traverses are initiated, in parallel, from the source node towards the destination node in the graph.
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45

Dong Ji-Yang, Zhang Jun-Ying, and Chen Zhong. "Autowave-competition neural network and its application to the single-source shortest-paths problem." Acta Physica Sinica 56, no. 9 (2007): 5013. http://dx.doi.org/10.7498/aps.56.5013.

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46

VENEMA, SVEN, HONG SHEN, and FRANCIS SURAWEERA. "NC ALGORITHMS FOR THE SINGLE MOST VITAL EDGE PROBLEM WITH RESPECT TO ALL PAIRS SHORTEST PATHS." Parallel Processing Letters 10, no. 01 (2000): 51–58. http://dx.doi.org/10.1142/s012962640000007x.

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For a weighted, undirected graph G=(V, E) where |V|=n and |E|=m, we examine the single most vital edge with respect to all-pairs shortest paths (APSP) under two different measurements. The first measurement considers only the impact of the removal of a single edge from the APSP on the shortest distance between each vertex pair. The second considers the total weight of all the edges which make up the APSP, that is, calculate the sum of the distance between each vertex pair after the deletion of any edge belonging to a shortest path. We give a sequential algorithm for this problem, and show how
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47

DE BERG, MARK, MARC VAN KREVELD, BENGT J. NILSSON, and MARK OVERMARS. "SHORTEST PATH QUERIES IN RECTILINEAR WORLDS." International Journal of Computational Geometry & Applications 02, no. 03 (1992): 287–309. http://dx.doi.org/10.1142/s0218195992000172.

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In this paper, a data structure is given for two and higher dimensional shortest path queries. For a set of n axis-parallel rectangles in the plane, or boxes in d-space, and a fixed target, it is possible with this structure to find a shortest rectilinear path avoiding all rectangles or boxes from any point to this target. Alternatively, it is possible to find the length of the path. The metric considered is a generalization of the L1-metric and the link metric, where the length of a path is its L1-length plus some (fixed) constant times the number of turns on the path. The data structure has
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48

D’Souza, Roshan M., Paul K. Wright, and Carlo Se´quin. "Handling Tool Holder Collision in Optimal Tool Sequence Selection for 2.5-D Pocket Machining." Journal of Computing and Information Science in Engineering 2, no. 4 (2002): 345–49. http://dx.doi.org/10.1115/1.1559154.

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Significant cycle time saving can be achieved in 2.5-D milling by intelligently selecting tool sequences. The problem of finding the optimal tool sequence was formulated as finding the shortest path in a single-source single-sink directed acyclic graph. The nodes in the graph represented the state of the stock after the tool named in the node was done machining and the edges represented the cost of machining. In this paper a novel method for handling tool holder collision in the graph-based algorithm for optimal tool sequence selection has been developed. The method consists of iteratively sol
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49

Himmich, Ilyas, Hatem Ben Amor, Issmail El Hallaoui, and François Soumis. "A Primal Adjacency-Based Algorithm for the Shortest Path Problem with Resource Constraints." Transportation Science 54, no. 5 (2020): 1153–69. http://dx.doi.org/10.1287/trsc.2019.0941.

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The shortest path problem with resource constraints (SPPRC) is often used as a subproblem within a column-generation approach for routing and scheduling problems. It aims to find a least-cost path between the source and the destination nodes in a network while satisfying the resource consumption limitations on every node. The SPPRC is usually solved using dynamic programming. Such approaches are effective in practice, but they can be inefficient when the network is large and especially when the number of resources is high. To cope with this major drawback, we propose a new exact primal algorit
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50

Sinyukov, Dmitry A., and Taşkin Padir. "CWave: Theory and Practice of a Fast Single-source Any-angle Path Planning Algorithm." Robotica 38, no. 2 (2019): 207–34. http://dx.doi.org/10.1017/s0263574719000560.

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SummaryPath planning on a two-dimensional grid is a well-studied problem in robotics. It usually involves searching for a shortest path between two vertices on a grid given that some of the grid cells are impassable (occupied by obstacles). Single-source path planning finds shortest paths from a given source vertex to all other vertices of the grid. Singles-source path planning enhances robot autonomy by calculating multiple possible paths for various navigation scenarios when the destination state is unknown. A high-performance algorithm for single-source any-angle path planning on a grid cal
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