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

Ade Candra, M. A. Budiman, Kevin Hartanto
{"title":"Dijkstra's and A-Star in Finding the Shortest Path: a Tutorial","authors":"Ade Candra, M. A. Budiman, Kevin Hartanto","doi":"10.1109/DATABIA50434.2020.9190342","DOIUrl":null,"url":null,"abstract":"As one form of the greedy algorithm, Dijkstra's can handle the shortest path search with optimum result in longer search time. Dijkstra's is contrary to A-Star, a best-first search algorithm, which can handle the shortest path search with a faster time but not always optimum. By looking at the advantages and disadvantages of Dijkstra's and A-Star, this tutorial discusses the implementation of the two algorithms in finding the shortest path in routes selection between 24 SPBU (gas stations). The routes are located in Medan City and represented in a directed graph. Moreover, the authors compare Dijkstra's and A-star based on the complexity of Big-Theta (Θ) and running time. The results show that the shortest path search between SPBU can be solved with Dijkstra's and A-Star, where in some cases, the routes produced by the two algorithms are different so that the total distance generated is also different. In this case, the running time of A-Star is proven to be faster than Dijkstra's, and it is following A-Star principle which selects the location point based on the best heuristic value while Dijkstra's does not. For the complexity, Dijkstra's is $\\Theta(\\mathrm{n}^{2})$ and A-Star is $\\Theta(\\mathrm{m}\\ast \\mathrm{n})$, where $0\\leq \\mathrm{m}\\leq \\mathrm{n}$.","PeriodicalId":165106,"journal":{"name":"2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DATABIA50434.2020.9190342","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 23

Abstract

As one form of the greedy algorithm, Dijkstra's can handle the shortest path search with optimum result in longer search time. Dijkstra's is contrary to A-Star, a best-first search algorithm, which can handle the shortest path search with a faster time but not always optimum. By looking at the advantages and disadvantages of Dijkstra's and A-Star, this tutorial discusses the implementation of the two algorithms in finding the shortest path in routes selection between 24 SPBU (gas stations). The routes are located in Medan City and represented in a directed graph. Moreover, the authors compare Dijkstra's and A-star based on the complexity of Big-Theta (Θ) and running time. The results show that the shortest path search between SPBU can be solved with Dijkstra's and A-Star, where in some cases, the routes produced by the two algorithms are different so that the total distance generated is also different. In this case, the running time of A-Star is proven to be faster than Dijkstra's, and it is following A-Star principle which selects the location point based on the best heuristic value while Dijkstra's does not. For the complexity, Dijkstra's is $\Theta(\mathrm{n}^{2})$ and A-Star is $\Theta(\mathrm{m}\ast \mathrm{n})$, where $0\leq \mathrm{m}\leq \mathrm{n}$.
Dijkstra's和a - star在寻找最短路径:教程
Dijkstra算法作为贪心算法的一种形式,可以在较长的搜索时间内处理最短路径搜索并获得最优结果。Dijkstra算法与a - star算法相反,a - star算法可以更快地处理最短路径搜索,但并不总是最优的。本教程通过分析Dijkstra算法和A-Star算法的优缺点,讨论了这两种算法在24个加油站(SPBU)之间的路线选择中寻找最短路径的实现。这些路线位于棉兰市,用有向图表示。此外,作者还根据Big-Theta的复杂性(Θ)和运行时间对Dijkstra和A-star进行了比较。结果表明,SPBU之间的最短路径搜索可以用Dijkstra算法和A-Star算法求解,但在某些情况下,两种算法产生的路径不同,从而产生的总距离也不同。在这种情况下,证明了A-Star算法的运行时间比Dijkstra算法快,并且遵循了基于最佳启发式值选择定位点的A-Star原则,而Dijkstra算法则没有。对于复杂性,Dijkstra的是$\Theta(\mathrm{n}^{2})$, A-Star的是$\Theta(\mathrm{m}\ast \mathrm{n})$,其中$0\leq \mathrm{m}\leq \mathrm{n}$。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信