Abstract
Optimization methods are used to develop a model that can decrease production costs and maximize performance. There are several strategies for optimizing in soft computing techniques, such as scent marking optimization, ant colony optimization, and machine learning optimization. Optimizing the current network model is our issue. This paper chooses optimization based on graph theory because this approach has a solid theoretical history with a systematic approach and proof. RailTel is one of the country's largest neutral telecom infrastructure providers that owns an exclusive Right of Way (ROW) Pan-India optic fiber network along the railway track. We carefully researched the RailTel optical network's topology and noted that the current model can be optimized and improved in specific parameters during our study. We found a few invariant graph parameters such as number of nodes, number of edges, average degree, average clustering diameter, transitivity, connectivity edge, average length of path, and density. We made an effort to upgrade the current model. When we compared both models, we found that the proposed model had almost the same or better values for all parameters. We succeeded in having a diameter of half the current model, an optimized average path length, and transitivity improvement. The current model showed better optimized results compare to the existing method.
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Amiripalli, S.S., Dora, T.V.S.A., Addagarla, S.K., Srinivasu, P.N., Rao, G.S.M. (2021). A Graph Invariant-Based TGO Model for RailTel Optical Networks. In: Dash, S.S., Panigrahi, B.K., Das, S. (eds) Sixth International Conference on Intelligent Computing and Applications . Advances in Intelligent Systems and Computing, vol 1369. Springer, Singapore. https://doi.org/10.1007/978-981-16-1335-7_7
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DOI: https://doi.org/10.1007/978-981-16-1335-7_7
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