Proceedings of International Conference on Applied Innovation in IT
2025/12/22 , Volume 13 , Issue 5 , pp.7 -14
An Improved AI-Driven Algorithm of Data Flow Optimization on the Normalized Bipolar Planar Free-Oriented Network Graph
Victor Tikhonov, Valery Sitnikov and Serhii Tikhonov Abstract: In this work, an improved algorithm has been developed for data flow optimization on the normalized bipolar planar network graph (NBPG) with free-oriented edges, that is taken as an enhanced model of a software defined network with flexibly reconfigured channels. The NBPG-model differs from known analogues by a special normalization of an arbitrary V-vertex graph structure as successive transforms of a primitive 3-vertex graph. Hereby, the searching for optimal paths in Maxflow problem yields a combinatorial task of constructing the full set of potentially optimal paths (POPs), for which a recursive heuristically solution can be found. Due to the NBPG-model generality, a complete set of potentially optimal paths on the uniform NBPG graph with Vmax vertices can be applied for any given NBGP(V) graph with V≤Vmax. The combinatorial task of POP searching on the normalized NBPG graph has quadratic complexity, and designing a program code for its computation is not a trivial case. To overcome this issue, AI have been involved for the explicit recursive constructing the full set of POPs on arbitrary normalized NBGP(V) graph via a public GPT-model training by the clustered combinatorial sequences. So, the optimal data flow distribution by criterion of maximum throughput on individual graphs is reduced to a simple cycle of processing a compact universal pre-defined set of potentially optimal paths that is similar for all the normalized graphs with parametrically limited vertex quantity. These features allow to simplify and speed up dynamic resource allocation in modern telecommunication networks with reconfigurable channels, expand their functionality and increase the quality of service when bidirectional multiproduct data transfer.
Keywords: Computer Network, Data Flow Optimization, Maxflow Problem, Free-oriented Graph, Combinatorics, Recursive Algorithm, Artificial Intelligence.
DOI: 10.25673/122800
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References:
- “Design & analysis of algorithms. Lecture 11,” 2023, [Online]. Available: https://www.cs.cmu.edu/
- G. Miller, “15-451: Algorithms. Lecture notes: Introduction to max flow,” 2019, [Online]. Available: https://www.cs.cmu.edu/
- “Max flow problem introduction,” 2025, [Online]. Available: https://www.geeksforgeeks.org/dsa/max-flow-problem-introduction/.
- L. Mouatadid, “CSC 373 - Algorithm design, analysis, and complexity,” 2016, [Online]. Available: https://www.cs.toronto.edu/
- L. R. Ford and D. R. Fulkerson, “Maximal flow through a network,” Canadian Journal of Mathematics, vol. 8, pp. 399–404, 1956.
- J. Edmonds and R. M. Karp, “Theoretical improvements in algorithmic efficiency for network flow problems,” Journal of the ACM, vol. 19, no. 2, pp. 248–264, 1972.
- “The Edmonds-Karp algorithm,” 2025, [Online]. Available: https://web.cs.dal.ca/
- E. A. Dinic, “Algorithm for solution of a problem of maximum flow in a network with power estimation,” Doklady Akademii Nauk SSSR, vol. 194, no. 4, pp. 1277–1280, 1970.
- A. V. Goldberg and R. E. Tarjan, “A new approach to the maximum flow problem,” in Proc. ACM Symp. Theory of Computing (STOC), 1986, p. 136.
- A. V. Goldberg, “The binary blocking flow algorithm,” in DIMACS Implementation Challenge, 2008.
- A. V. Goldberg and S. Rao, “Beyond the flow decomposition barrier,” Journal of the ACM, vol. 45, no. 5, pp. 783–797, 1998.
- D. Papp, “The Goldberg–Rao algorithm for the maximum flow problem,” 2006, [Online]. Available: https://www.cs.princeton.edu/courses/archive/fall06/cos528/handouts/Goldberg-Rao.pdf.
- P. Christiano et al., “Electrical flows, Laplacian systems, and faster approximation of maximum flow in undirected graphs,” 2010, [Online]. Available: https://www.semanticscholar.org/reader/af1afe809c106ed2618fc2ae14b696f672fc4bc1.
- J. Albuquerque et al., “Flexible channel model configuration for scalable 5G-LENA simulations,” in Proc. Int. Conf. ns-3 (ICNS3), 2025, pp. 125–133.
- V. Arlunno et al., “Digital non-linear equalization for flexible capacity ultradense WDM channels for metro core networking,” Optics Express, vol. 19, no. 26, pp. 270–276, 2011.
- M. Radjuh, “Osobennosti primenenia principa maximuma Pontriagina,” 2013, [Online]. Available: https://rep.bntu.by/handle/data/5527.
- J. A. Bondy and U. S. R. Murty, Graph Theory with Applications, 1976, [Online]. Available: https://www.iro.umontreal.ca/
- D. P. Williamson, Network flow algorithms, Cornell University, 2019, [Online]. Available: https://www.networkflowalgs.com/book.pdf.
- O. Tykhonova et al., “The max-flow problem statement on the three-pole open network graph,” in Proc. Int. Conf. Advanced Information and Communications Technologies (AICT), 2019, pp. 209–212.
- O. Tikhonova, “Conveyor-modular method of multimedia flows integration with delay control in packet-based telecommunication networks,” Ph.D. dissertation, Odessa, 2019.
- R. Haese et al., “Algorithms and complexity for the almost equal maximum flow problem,” 2021, [Online]. Available: https://arxiv.org/pdf/2104.05288.
- N. Alzaben and D. Engels, “End-to-end routing in SDN controllers using max-flow min-cut route selection algorithm,” in Proc. Int. Conf. Advanced Communication Technology (ICACT), 2021, pp. 461–467.
- H. Liu et al., “Optimization of a logistics transportation network based on a genetic algorithm,” in Mobile Internet of Things (IoT) Multi-sensor Data Fusion, 2022.
- O. Cruz-Mejía and A. Letchford, “A survey on exact algorithms for the maximum flow and minimum-cost flow problems,” Networks, vol. 82, pp. 167–176, 2023.
- J. B. Orlin, “Max flows in O(nm) time, or better,” in Proc. 45th Annu. ACM Symp. Theory of Computing (STOC), Palo Alto, CA, USA, Jun. 2013, pp. 765–774, doi: 10.1145/2488608.2488705.
- J. B. Orlin and X. Gong, “A fast maximum flow algorithm,” Networks, vol. 77, no. 4, pp. 287–321, 2021, doi: 10.1002/net.22001.
- K. P. K. Madicharla, “Human-in-the-loop LLMOps: Balancing automation and control,” WJAETS, vol. 15, no. 2, pp. 1992–1999, 2025.
- J. Turgunov et al., “Human-in-the-loop systems for ethical AI,” 2025, [Online]. Available: https://www.researchgate.net/publication/393802734_HUMAN-IN-THE-LOOP_SYSTEMS_FOR_ETHICAL_AI.
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