Targeted multiobjective Dijkstra algorithm

IF 1.6 4区计算机科学 Q4 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Networks Pub Date : 2023-07-08 DOI:10.1002/net.22174

Pedro Maristany de las Casas, Luitgard Kraus, A. Sedeño-Noda, R. Borndörfer

引用次数: 7

Abstract

We introduce the Targeted Multiobjective Dijkstra Algorithm (T‐MDA), a label setting algorithm for the One‐to‐One Multiobjective Shortest Path (MOSP) Problem. It is based on the recently published Multiobjective Dijkstra Algorithm (MDA) and equips it with A*‐like techniques. For any explored subpath, a label setting MOSP algorithm decides whether the subpath can be discarded or must be stored as part of the output. A major design choice is how to store subpaths from the moment they are first explored until the mentioned final decision can be made. The T‐MDA combines the polynomially bounded size of the priority queue used in the MDA and a lazy management of paths that are not in the queue. The running time bounds from the MDA remain valid. In practice, the T‐MDA outperforms known algorithms from the literature and the increased memory consumption is negligible. In this paper, we benchmark the T‐MDA against an improved version of the state of the art NAMOAdr∗$$ {\mathrm{NAMOA}}_{\mathrm{dr}}^{\ast } $$ One‐to‐One MOSP algorithm from the literature on a standard testbed.

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目标多目标Dijkstra算法

我们介绍了目标多目标Dijkstra算法（T-MDA），这是一种用于一对一多目标最短路径（MOSP）问题的标签设置算法。它基于最近发表的多目标Dijkstra算法（MDA），并为其配备了类似A*的技术。对于任何探索的子路径，标签设置MOSP算法决定子路径是可以丢弃还是必须作为输出的一部分存储。一个主要的设计选择是如何存储子路径，从第一次探索到做出上述最终决定。T‐MDA结合了MDA中使用的优先级队列的多项式有界大小和不在队列中的路径的惰性管理。MDA的运行时间界限仍然有效。在实践中，T-MDA优于文献中的已知算法，并且增加的内存消耗可以忽略不计。在本文中，我们将T‐MDA与标准试验台上文献中的现有NAMOAdr*$$｛\mathrm｛NAMOA｝｝_｛\math rm｛dr｝^｛\ast｝$$一对一MOSP算法的改进版本进行了比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Networks 工程技术-计算机：硬件

CiteScore

4.40

自引率

9.50%

发文量

审稿时长

12 months

期刊介绍： Network problems are pervasive in our modern technological society, as witnessed by our reliance on physical networks that provide power, communication, and transportation. As well, a number of processes can be modeled using logical networks, as in the scheduling of interdependent tasks, the dating of archaeological artifacts, or the compilation of subroutines comprising a large computer program. Networks provide a common framework for posing and studying problems that often have wider applicability than their originating context. The goal of this journal is to provide a central forum for the distribution of timely information about network problems, their design and mathematical analysis, as well as efficient algorithms for carrying out optimization on networks. The nonstandard modeling of diverse processes using networks and network concepts is also of interest. Consequently, the disciplines that are useful in studying networks are varied, including applied mathematics, operations research, computer science, discrete mathematics, and economics. Networks publishes material on the analytic modeling of problems using networks, the mathematical analysis of network problems, the design of computationally eﬃcient network algorithms, and innovative case studies of successful network applications. We do not typically publish works that fall in the realm of pure graph theory (without signiﬁcant algorithmic and modeling contributions) or papers that deal with engineering aspects of network design. Since the audience for this journal is then necessarily broad, articles that impact multiple application areas or that creatively use new or existing methodologies are especially appropriate. We seek to publish original, well-written research papers that make a substantive contribution to the knowledge base. In addition, tutorial and survey articles are welcomed. All manuscripts are carefully refereed.