Facilitating Large-Scale Graph Search Algorithms with Lock-Free Concurrent Pairing Heaps

This paper introduces a lock-free version of a Pairing heap. Dijkstra's algorithm is a search algorithm to solve the single-source shortest path problem. The performance of Dijkstra's algorithm improves when threads can also perform work concurrently (in particular, when decreaseKey calls occur concurrently.) However, current implementations of decreaseKey on popular backing data structures such as Pairing heaps and Fibonacci heaps severely limit concurrency. Lock-free techniques can improve the concurrency of search structures such as heaps. In this paper we introduce decreaseKey and insert operators for Pairing heaps that provide lock-free guarantees while still running in constant time.

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SISTEM PENDUKUNG KEPUTUSAN OTOMATISASI PLANNING WISATA BERBASIS WEBSITE DENGAN MENGGUNAKAN METODE DIJKSTRA

Compiler ◽

10.28989/compiler.v2i1.34 ◽

2013 ◽

Vol 2 (1) ◽

Author(s):

Eko Suryanto ◽

Anton Setiawan Honggowibowo ◽

Nurcahyani Dewi Retnowati

Keyword(s):

Shortest Path ◽

Search Algorithm ◽

Graph Search ◽

Single Source ◽

Application Development ◽

Tourism Planning ◽

Shortest Path Tree

The application development of website that have a good progress from Website is very important in tourism field where website can promote the interesting and unique of tourism place for tourist. In tourism field website can be important if completed with superriority is tourism planning where tourist can plan their holidays effective and efficient. Dijkstra method is a graph search algorithm is used to solve the problem with a single source shortest path on a graph that produces a shortest path tree. The usage of dijkstra method in decision the tourism planning base website is appropriate because used to plan the holidays based on the route or the sorthest way that has been found by latitude and longitude counting which is very important to decide range from each place, so it can help the tourist.

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Optimized Graph Search Algorithms for Exploration with Mobile Robot

EMITTER International Journal of Engineering Technology ◽

10.24003/emitter.v9i2.614 ◽

2021 ◽

Vol 9 (2) ◽

pp. 222-238

Author(s):

Aydın GULLU ◽

Hilmi KUŞÇU

Keyword(s):

Mobile Robot ◽

Shortest Path ◽

Hybrid Algorithm ◽

Experimental Studies ◽

Search Algorithms ◽

Graph Search ◽

Dijkstra Algorithm ◽

Unknown Environment ◽

Unknown Environments ◽

Graph Search Algorithms

Graph search algorithms and shortest path algorithms, designed to allow real mobile robots to search unknown environments, are typically run in a hybrid manner, which results in the fast exploration of an entire environment using the shortest path. In this study, a mobile robot explored an unknown environment using separate depth-first search (DFS) and breadth-first search (BFS) algorithms. Afterward, developed DFS + Dijkstra and BFS + Dijkstra algorithms were run for the same environment. It was observed that the newly developed hybrid algorithm performed the identification using less distance. In experimental studies with real robots, progression with DFS for the first-time discovery of an unknown environment is very efficient for detecting boundaries. After finding the last point with DFS, the shortest route was found with Dijkstra for the robot to reach the previous node. In defining a robot that works in a real environment using DFS algorithm for movement in unknown environments and Dijkstra algorithm in returning, time and path are shortened. The same situation was tested with BFS and the results were examined. However, DFS + Dijkstra was found to be the best algorithm in field scanning with real robots. With the hybrid algorithm developed, it is possible to scan the area with real autonomous robots in a shorter time. In this study, field scanning was optimized using hybrid algorithms known.

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FAST IMPLEMENTATION OF DIJKSTRA'S ALGORITHM FOR THE LARGE-SCALE SHORTEST PATH PROBLEM

Transactions of the Operations Research Society of Japan ◽

10.15807/torsj.54.58 ◽

2011 ◽

Vol 54 (0) ◽

pp. 58-83

Author(s):

Yuichiro Yasui ◽

Katsuki Fujisawa ◽

Hiroshi Sasajima ◽

Kazushige Goto

Keyword(s):

Shortest Path ◽

Large Scale ◽

Shortest Path Problem ◽

Dijkstra’S Algorithm ◽

Dijkstra's Algorithm ◽

Fast Implementation

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Finding Shortest Path Basing on Dijkstra's Algorithm For Baghdad Universities by using Geographic Information System Applications

10.25212/icoit17.030 ◽

2017 ◽

Author(s):

Roaa Ebada Saeed ◽

Amer Saleem Elameer ◽

Rafah Shahab Alhamdani

Keyword(s):

Information System ◽

Geographic Information System ◽

Shortest Path ◽

Geographic Information ◽

Dijkstra’S Algorithm ◽

Dijkstra's Algorithm

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Finding A Small Vertex Cover in Massive Sparse Graphs: Construct, Local Search, and Preprocess

Journal of Artificial Intelligence Research ◽

10.1613/jair.5443 ◽

2017 ◽

Vol 59 ◽

pp. 463-494 ◽

Cited By ~ 6

Author(s):

Shaowei Cai ◽

Jinkun Lin ◽

Chuan Luo

Keyword(s):

Local Search ◽

Real World ◽

Large Scale ◽

Heuristic Algorithms ◽

Search Algorithm ◽

Vertex Cover ◽

Search Algorithms ◽

Theory And Practice ◽

Sparse Graphs ◽

Massive Graphs

The problem of finding a minimum vertex cover (MinVC) in a graph is a well known NP-hard combinatorial optimization problem of great importance in theory and practice. Due to its NP-hardness, there has been much interest in developing heuristic algorithms for finding a small vertex cover in reasonable time. Previously, heuristic algorithms for MinVC have focused on solving graphs of relatively small size, and they are not suitable for solving massive graphs as they usually have high-complexity heuristics. This paper explores techniques for solving MinVC in very large scale real-world graphs, including a construction algorithm, a local search algorithm and a preprocessing algorithm. Both the construction and search algorithms are based on low-complexity heuristics, and we combine them to develop a heuristic algorithm for MinVC called FastVC. Experimental results on a broad range of real-world massive graphs show that, our algorithms are very fast and have better performance than previous heuristic algorithms for MinVC. We also develop a preprocessing algorithm to simplify graphs for MinVC algorithms. By applying the preprocessing algorithm to local search algorithms, we obtain two efficient MinVC solvers called NuMVC2+p and FastVC2+p, which show further improvement on the massive graphs.

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Journey to Crime Using Dijkstra’s Algorithm

Nigerian Journal of Environmental Sciences and Technology - March 2018 ◽

10.36263/nijest.2017.01.0019a ◽

2017 ◽

Vol 1 (1) ◽

pp. 1-14

Author(s):

J. O. Olusina ◽

J. B. Olaleye

Keyword(s):

Information Systems ◽

Geographic Information Systems ◽

Shortest Path ◽

Geographic Information ◽

Service Area ◽

Dijkstra’S Algorithm ◽

Crime Mapping ◽

Cost Matrix ◽

Dijkstra's Algorithm ◽

Journey To Crime

This paper describes some benefits of crime mapping in a Geographic Information Systems (G.I.S.) environment. The underlining principle of Journey to Crime was discussed. Crime Spots and Police Stations in the study area were mapped, Shortest-Path, Closest Facility, Service Area and OD (Origin – Destination) Cost Matrix were determined based on Dijkstra's Algorithm. Results show that the distribution of police stations does not correspond with the spread of crime spots.

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