The Shortest Path Problem and Its Critical Edge in Uncertain Environment

Jie, Ke Wei; Zhao, Gao Chang; Sun, Xiao Jun

doi:10.1109/access.2019.2948958

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…Kou, et al (2019) [49] propose two algorithms for a single-constrained SPP. Jie, et al (2019) [50] establish an information transmission time expectation-variance shortest path model and a fuzzy shortest path critical edge model. An improved ant colony algorithm with traffic congestion factor and random variables is developed to solve the SPP in an uncertain environment.…”

Section: Introductionmentioning

confidence: 99%

An Improved Discrete Jaya Algorithm for Shortest Path Problems in Transportation-Related Processes

et al. 2023

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Shortest path problems are encountered in many engineering applications, e.g., intelligent transportation, robot path planning, and smart logistics. The environmental changes as sensed and transmitted via the Internet of Things make the shortest path change frequently, thus posing ever-increasing difficulty for traditional methods to meet the real-time requirements of many applications. Therefore, developing more efficient solutions has become particularly important. This paper presents an improved discrete Jaya algorithm (IDJaya) to solve the shortest path problem. A local search operation is applied to expand the scope of solution exploration and improve solution quality. The time complexity of IDJaya is analyzed. Experiments are carried out on seven real road networks and dense graphs in transportation-related processes. IDJaya is compared with the Dijkstra and ant colony optimization (ACO) algorithms. The results verify the superiority of the IDJaya over its peers. It can thus be well utilized to meet real-time application requirements.

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