scholarly journals Greedy, A-Star, and Dijkstra’s Algorithms in Finding Shortest Path

2021 ◽  
Vol 2 (1) ◽  
pp. 45-52
Author(s):  
Muhammad Rhifky Wayahdi ◽  
Subhan Hafiz Nanda Ginting ◽  
Dinur Syahputra
Keyword(s):  
Greedy Algorithm ◽  
Shortest Path ◽  
Optimal Solution ◽  
The Other ◽  
Complex Data ◽  
Dijkstra’S Algorithm ◽  
Dijkstra's Algorithm ◽  
The Greedy Algorithm ◽  
Better Than

The problem of finding the shortest path from a path or graph has been quite widely discussed. There are also many algorithms that are the solution to this problem. The purpose of this study is to analyze the Greedy, A-Star, and Dijkstra algorithms in the process of finding the shortest path. The author wants to compare the effectiveness of the three algorithms in the process of finding the shortest path in a path or graph. From the results of the research conducted, the author can conclude that the Greedy, A-Star, and Dijkstra algorithms can be a solution in determining the shortest path in a path or graph with different results. The Greedy algorithm is fast in finding solutions but tends not to find the optimal solution. While the A-Star algorithm tends to be better than the Greedy algorithm, but the path or graph must have complex data. Meanwhile, Dijkstra's algorithm in this case is better than the other two algorithms because it always gets optimal results.

scholarly journals Journey to Crime Using Dijkstra’s Algorithm

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.

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

2019 ◽  
Author(s):  
Jeremy Mayeres ◽  
Charles Newton ◽  
Helena Arpudaraj
Keyword(s):  
Data Structures ◽  
Shortest Path ◽  
Large Scale ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Graph Search ◽  
Dijkstra’S Algorithm ◽  
Single Source ◽  
Dijkstra's Algorithm ◽  
Graph Search Algorithms

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.

scholarly journals The Implementation of Kruskal’s Algorithm for Minimum Spanning Tree in a Graph

2021 ◽  
Vol 348 ◽  
pp. 01001
Author(s):  
Paryati ◽  
Krit Salahddine
Keyword(s):  
Shortest Path ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Dijkstra’S Algorithm ◽  
Dijkstra Algorithm ◽  
Application System ◽  
Path Creation ◽  
Dijkstra's Algorithm

Kruskal’s Algorithm is an algorithm used to find the minimum spanning tree in graphical connectivity that provides the option to continue processing the least-weighted margins. In the Kruskal algorithm, ordering the weight of the ribs makes it easy to find the shortest path. This algorithm is independent in nature which will facilitate and improve path creation. Based on the results of the application system trials that have been carried out in testing and comparisons between the Kruskal algorithm and the Dijkstra algorithm, the following conclusions can be drawn: that a strength that is the existence of weight sorting will facilitate the search for the shortest path. And considering the characteristics of Kruskal’s independent algorithm, it will facilitate and improve the formation of the path. The weakness of the Kruskal algorithm is that if the number of nodes is very large, it will be slower than Dijkstra’s algorithm because it has to sort thousands of vertices first, then form a path.

Dijkstra’s-DBSCAN: Fast, Accurate, and Routable Density Based Clustering of Traffic Incidents on Large Road Network

2018 ◽  
Vol 2672 (45) ◽  
pp. 265-273
Author(s):  
Yang Zhang ◽  
Lee D. Han ◽  
Hyun Kim
Keyword(s):  
Shortest Path ◽  
Road Network ◽  
Clustering Algorithm ◽  
Computational Cost ◽  
Incident Management ◽  
Dijkstra’S Algorithm ◽  
Hotspot Detection ◽  
Traffic Incidents ◽  
Original Algorithm ◽  
Dijkstra's Algorithm

Incident hotspots are used as a direct indicator of the needs for road maintenance and infrastructure upgrade, and an important reference for investment location decisions. Previous incident hotspot identification methods are all region based, ignoring the underlying road network constraints. We first demonstrate how region based hotspot detection may be inaccurate. We then present Dijkstra’s-DBSCAN, a new network based density clustering algorithm specifically for traffic incidents which combines a modified Dijkstra’s shortest path algorithm with DBSCAN (density based spatial clustering of applications with noise). The modified Dijkstra’s algorithm, instead of returning the shortest path from a source to a target as the original algorithm does, returns a set of nodes (incidents) that are within a requested distance when traveling from the source. By retrieving the directly reachable neighbors using this modified Dijkstra’s algorithm, DBSCAN gains its awareness of network connections and measures distance more practically. It avoids clustering incidents that are close but not connected. The new approach extracts hazardous lanes instead of regions, and so is a much more precise approach for incident management purposes; it reduces the [Formula: see text] computational cost to [Formula: see text], and can process the entire U.S. network in seconds; it has routing flexibility and can extract clusters of any shape and connections; it is parallellable and can utilize distributed computing resources. Our experiments verified the new methodology’s capability of supporting safety management on a complicated surface street configuration. It also works for customized lane configuration, such as freeways, freeway junctions, interchanges, roundabouts, and other complex combinations.

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