Dijkstra's and A-Star in Finding the Shortest Path: a Tutorial

Candra, Ade; Budiman, Mohammad Andri; Hartanto, Kevin

doi:10.1109/databia50434.2020.9190342

Cited by 76 publications

(33 citation statements)

References 2 publications

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“…A. Candra et al(2020) [11] states that Dijkstra's can handle the shortest path search with the best results in a longer search time as one variation of the greedy algorithm. The best-first search method A-Star, which can handle the shortest path search with a faster time but is not always optimal, contrasts with Dijkstra's.…”

Section: IIImentioning

confidence: 99%

VR Based Remote Campus Tour

Kadav¹,

Patwari²,

Kalme³

et al. 2023

IJRASET

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We recommend a system called "Virtual Tour of College Infrastructure" that gives a virtual college tour to visitors, such as students, parents, and guests. They may be interested in learning about the facilities and infrastructure offered by the college. For this, students must really travel to the college campus, which will take a lot of their time and effort and cost money for those who live far away. The suggested solution offers a virtual perspective of the infrastructure at the college in order to address this problem. This enables visitors access a certain department or lab from a distance. Additionally, it can be displayed at the College's main gate to assist visitors in finding a certain student, member of staff, or lab by outlining the precise route through the "Search" module. Virtual reality is essentially how this technology operates. Combining two or more photos to create an image panorama is the procedure. The method of producing an image panorama from a collection of photos with overlapped fields is known as image stitching. The process of stitching together photographs is difficult because of issues including noise-corrupted images, indexing a lot of images, high image resolution, parallax, and scene motion. Another difficulty is getting rid of seams that are noticeable. This work applied a feature-based automatic image stitching approach to obtain a broad seamless panorama.

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Section: IIImentioning

confidence: 99%

VR Based Remote Campus Tour

Kadav¹,

Patwari²,

Kalme³

et al. 2023

IJRASET

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“…The formula of A* algorithm is shown as follows:

where

is the cost estimation from the initial state to the target state via state n ,

is the actual cost from the initial state to the state n in the state space, and

is the estimated cost of the best path from state n to target state [ 50 ]. The overall flow chart of A* algorithm is shown in Figure 1 , and the related steps are as follows [ 50 ]:…”

Section: Three-dimensional Distance Vector-hop (3ddv-hop) and A* Amentioning

confidence: 99%

Three-Dimensional Localization Algorithm Based on Improved A* and DV-Hop Algorithms in Wireless Sensor Network

Huang

Han

Cui

et al. 2021

Sensors

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In the traditional wireless sensor networks (WSNs) localization algorithm based on the Internet of Things (IoT), the distance vector hop (DV-Hop) localization algorithm has the disadvantages of large deviation and low accuracy in three-dimensional (3D) space. Based on the 3DDV-Hop algorithm and combined with the idea of A* algorithm, this paper proposes a wireless sensor network node location algorithm (MA*-3DDV-Hop) that integrates the improved A* algorithm and the 3DDV-Hop algorithm. In MA*-3DDV-Hop, firstly, the hop-count value of nodes is optimized and the error of average distance per hop is corrected. Then, the multi-objective optimization non dominated sorting genetic algorithm (NSGA-II) is adopted to optimize the coordinates locally. After selection, crossover, mutation, the Pareto optimal solution is obtained, which overcomes the problems of premature convergence and poor convergence of existing algorithms. Moreover, it reduces the error of coordinate calculation and raises the localization accuracy of wireless sensor network nodes. For three different multi-peak random scenes, simulation results show that MA*-3DDV-Hop algorithm has better robustness and higher localization accuracy than the 3DDV-Hop, PSO-3DDV-Hop, GA-3DDV-Hop, and N2-3DDV-Hop.

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“…The A-Star algorithm is a classic path search algorithm and can be used to search mazes [18]. The A-Star algorithm guides the optimal path to the goal if the heuristic function h(n) is acceptable, meaning that it will never overestimates the original cost [19] or actual cost [20]. Evaluation function f(n) = g(n) + h(n), where [17][21] [22]: g(n) = cost so far to reach n. h(n) = estimated cost from n to target (goal).…”

Section: A-star Algorithmmentioning

confidence: 99%

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

Wayahdi

Ginting

Syahputra

2021

Int. J. Adv. Data Inf. Syst.

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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.

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Dijkstra's and A-Star in Finding the Shortest Path: a Tutorial

Cited by 76 publications

References 2 publications

VR Based Remote Campus Tour

VR Based Remote Campus Tour

Three-Dimensional Localization Algorithm Based on Improved A* and DV-Hop Algorithms in Wireless Sensor Network

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

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