An efficient all-pairs approach for multi-objective dynamic shortest path problems

The Shortest Path problem is fundamental for determining optimal routes in various applications. The Dynamic Shortest Path problem extends this concept to evolving graph structures. However, existing algorithms often fail to address decision-making complexities involving multiple objectives. In our...

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Bibliographic Details
Published in:Neural computing & applications Vol. 37; no. 30; pp. 24823 - 24851
Main Authors: da Silva, Juarez Machado, Ramos, Gabriel de Oliveira, Barbosa, Jorge Luis Victória
Format: Journal Article
Language:English
Published: London Springer London 01.10.2025
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Decision making
Energy consumption
Image Processing and Computer Vision
Labeling
Multiple objective analysis
Objectives
Probability and Statistics in Computer Science
S.I.: Multi-Objective Decision Making 2023
Shortest-path problems
Special Issue on Multi-Objective Decision Making 2023
Decision-making
Shortest path
Dynamic shortest paths
Multi-objective
Multi-objective shortest paths
ISSN:0941-0643, 1433-3058
Online Access:Get full text
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Summary:The Shortest Path problem is fundamental for determining optimal routes in various applications. The Dynamic Shortest Path problem extends this concept to evolving graph structures. However, existing algorithms often fail to address decision-making complexities involving multiple objectives. In our previous work, we introduced the Multi-Objective Dynamic Shortest Path problem to address this gap. This paper presents the All-pairs Multi-objective Dynamic Shortest Path algorithm, offering a novel approach that combines a labeling-correcting method with the Optimistic Linear Support algorithm. This hybrid methodology enhances efficiency by minimizing redundant calculations during graph updates. Extensive testing demonstrates that our algorithm is over 3.22 times faster than baseline algorithms in producing Pareto solutions. This work advances techniques for multi-objective dynamic shortest paths and tackles challenges in evolving graph structures, paving the way for future research in this dynamic field.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-025-11437-6