A new exact algorithm for the shortest path problem: An optimized shortest distance matrix
Computers & Industrial Engineering, ISSN: 0360-8352, Vol: 158, Page: 107407
2021
- 13Citations
- 31Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
The growing amount of data generated from the increasingly sophisticated network connections requires greater accuracy and higher efficiency in pinpointing the shortest paths concerned. Therefore, the earlier classical exact algorithms are no longer 100 percent suitable for large-scale data processing, for their known great time complexity during calculation. In this paper, We present an updated shortest distance matrix (SDM) algorithm. Evidence to the operation's properties is provided and the properties are used in subsequent optimizations. Compared with Dijkstra's algorithm and Floyd's algorithm, the optimized SDM algorithm with parallel mode makes a great improvement in shortening the running time. The data test shows that the new algorithm improves the efficiency in processing a large amount of data.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0360835221003119; http://dx.doi.org/10.1016/j.cie.2021.107407; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85107642322&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0360835221003119; https://dx.doi.org/10.1016/j.cie.2021.107407
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know