A Multi-Stage Metaheuristic Algorithm for Shortest Simple Path Problem With Must-Pass Nodes

Su, Zhouxing; Zhang, Junchen; Lu, Zhengding

doi:10.1109/access.2019.2908011

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…Ghaffari and Li [22] provided a distributed SSSP algorithm with less complexity, and it constitutes the first sublinear time algorithm for directed graphs. For the SSPP-MPN (Shortest Simple Path Problem with Must-Past Nodes), Su et al [23] proposed a multistage metaheuristic algorithm based on k-opt move, candidate path search, conflicting nodes promotion, and connectivity relaxation. e above algorithms do not consider the structural characteristics of multistage graphs, so they will produce a large amount of communication overhead, resulting in tedious execution time.…”

Section: Related Workmentioning

confidence: 99%

DMGA: A Distributed Shortest Path Algorithm for Multistage Graph

Cui

Liu

et al. 2021

Scientific Programming

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The multistage graph problem is a special kind of single-source single-sink shortest path problem. It is difficult even impossible to solve the large-scale multistage graphs using a single machine with sequential algorithms. There are many distributed graph computing systems that can solve this problem, but they are often designed for general large-scale graphs, which do not consider the special characteristics of multistage graphs. This paper proposes DMGA (Distributed Multistage Graph Algorithm) to solve the shortest path problem according to the structural characteristics of multistage graphs. The algorithm first allocates the graph to a set of computing nodes to store the vertices of the same stage to the same computing node. Next, DMGA calculates the shortest paths between any pair of starting and ending vertices within a partition by the classical dynamic programming algorithm. Finally, the global shortest path is calculated by subresults exchanging between computing nodes in an iterative method. Our experiments show that the proposed algorithm can effectively reduce the time to solve the shortest path of multistage graphs.

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Section: Related Workmentioning

confidence: 99%

DMGA: A Distributed Shortest Path Algorithm for Multistage Graph

Cui

Liu

et al. 2021

Scientific Programming

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“…Ganganath, et al (2018) [47] designed a heuristic search algorithm to find solutions to the shortest path on uneven terrains. Su, et al (2019) [48] propose a multi-stage meta-heuristic algorithm for solving the SPP with must-pass nodes. Kou, et al (2019) [49] propose two algorithms for a single-constrained SPP.…”

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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