A Study on Rapid Incremental Maximum Flow Algorithm in Dynamic Network

Wang, Yuanyuan; Ling, Jianhui; Zhou, Sihai; Liu, Yundi; Liao, Wei-Cheng; Zhang, Baili

doi:10.1109/ic3.2018.00011

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…The augmenting path is where the path will be found from the start to the end of the flow and then will send the flow into the paths found (Pang et al, 2018;Peng, Lin, Huang, Zou, & Yang, 2017). These paths are from the networks that have simple paths wherein the original network the damaged network was included (Wang et al, 2018). While the pre-flow push is a flood graph where there is an incoming and outgoing flow of nodes without the constraint of the mass balance (Pang et al, 2018).…”

Section: Literature Surveymentioning

confidence: 99%

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Gap Analysis of Ford-Fulkerson Algorithm and Edmonds-Karp Algorithm as Machine Learning Approach for Augmentation Path in the Maximum Flow Problem

Dumlao,

Banal,

Livara

2021

ijitgeb

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The maximum flow problem is popular with many researchers as it plays a significant role in many areas. This problem is about obtaining the maximum flow where there is one source and sink in the network flow. In solving maximum flow problems, many algorithms are applied such as the most used are the Ford-Fulkerson Algorithm and the Edmonds-Karp Algorithm. The goal of this study is to identify the gaps of these two algorithms and sufficiently provide an analysis and comparison of their time complexity, simplicity, and effectiveness to solve the augmentation path in the maximum flow problem. The Ford-Fulkerson and Edmonds-Karp algorithms performed well and sufficiently to solve the augmentation path in the maximum flow problem. However, both algorithms consist of different gaps like having slow time complexity and difficulty in implementation and execution. Keywords: Maximum Flow Problem, Augmentation Path, Ford Fulkerson Algorithm, Network Flow, Edmonds-Karp Algorithm

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Section: Literature Surveymentioning

confidence: 99%

“…In addition, the max flow algorithm also has Dinic, the shortest path combination algorithm, and the Edmonds-Karp algorithm. These algorithms have the same purpose of maximizing the flow of the augmented path, but they also have different features that were improved to solve the max flow problems (Wang et al, 2018).…”

Section: Literature Surveymentioning

confidence: 99%