A New Approach for Automatic Search for Families of Optimal Undirected Double-Loop Networks

Based on the analysis of a large dataset for optimal undirected double-loop networks, we studied the problem of finding families of optimal double-loop graphs with the minimal possible diameter. Optimal double-loop networks are of practical interest as graph models for reliable, low-delay communicat...

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Main Authors: Emilia A. Monakhova, Oleg G. Monakhov, Edward R. Rzaev, Aleksandr A. Amerikanov, Mikhail Y. Romashikhin, Aleksandr Y. Romanov
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
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Online Access:https://ieeexplore.ieee.org/document/11036794/
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author Emilia A. Monakhova
Oleg G. Monakhov
Edward R. Rzaev
Aleksandr A. Amerikanov
Mikhail Y. Romashikhin
Aleksandr Y. Romanov
author_facet Emilia A. Monakhova
Oleg G. Monakhov
Edward R. Rzaev
Aleksandr A. Amerikanov
Mikhail Y. Romashikhin
Aleksandr Y. Romanov
author_sort Emilia A. Monakhova
collection DOAJ
description Based on the analysis of a large dataset for optimal undirected double-loop networks, we studied the problem of finding families of optimal double-loop graphs with the minimal possible diameter. Optimal double-loop networks are of practical interest as graph models for reliable, low-delay communication in computer systems and networks-on-chip, due to their advantageous networking properties. A large dataset of optimal double-loop networks with up to 50000 nodes and all the optimal generators for given graph orders were built. We automated the process of searching for analytical (described by polynomials in diameter) descriptions of the families of optimal double-loop networks. After applying the integration of differential evolution methods, exhaustive local search, and a new search algorithm (based on the division of graph description polynomials) to the analysis of the generated dataset, we found a large amount of new (distinct from all previously known) families of optimal double-loop networks with generators of linear or quadratic types. Based on the analysis of the constructed dataset, a method for accelerated search for finding descriptions of optimal double-loop networks using second-order surface approximation was proposed. Our new approach can be used for the analysis of datasets of other promising classes of graphs and networks.
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institution Kabale University
issn 2169-3536
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publishDate 2025-01-01
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spelling doaj-art-ce68facab70048cbbf2ad6bd47e8fb8d2025-08-20T03:31:27ZengIEEEIEEE Access2169-35362025-01-011310471610472710.1109/ACCESS.2025.357979011036794A New Approach for Automatic Search for Families of Optimal Undirected Double-Loop NetworksEmilia A. Monakhova0https://orcid.org/0000-0002-6814-4655Oleg G. Monakhov1https://orcid.org/0000-0001-8316-418XEdward R. Rzaev2https://orcid.org/0000-0002-3504-7429Aleksandr A. Amerikanov3https://orcid.org/0000-0002-5970-2125Mikhail Y. Romashikhin4https://orcid.org/0009-0005-1604-3333Aleksandr Y. Romanov5https://orcid.org/0000-0002-9410-9431Institute of Computational Mathematics and Mathematical Geophysics of SB RAS, Novosibirsk, RussiaInstitute of Computational Mathematics and Mathematical Geophysics of SB RAS, Novosibirsk, RussiaHSE University, Moscow, RussiaHSE University, Moscow, RussiaHSE University, Moscow, RussiaHSE University, Moscow, RussiaBased on the analysis of a large dataset for optimal undirected double-loop networks, we studied the problem of finding families of optimal double-loop graphs with the minimal possible diameter. Optimal double-loop networks are of practical interest as graph models for reliable, low-delay communication in computer systems and networks-on-chip, due to their advantageous networking properties. A large dataset of optimal double-loop networks with up to 50000 nodes and all the optimal generators for given graph orders were built. We automated the process of searching for analytical (described by polynomials in diameter) descriptions of the families of optimal double-loop networks. After applying the integration of differential evolution methods, exhaustive local search, and a new search algorithm (based on the division of graph description polynomials) to the analysis of the generated dataset, we found a large amount of new (distinct from all previously known) families of optimal double-loop networks with generators of linear or quadratic types. Based on the analysis of the constructed dataset, a method for accelerated search for finding descriptions of optimal double-loop networks using second-order surface approximation was proposed. Our new approach can be used for the analysis of datasets of other promising classes of graphs and networks.https://ieeexplore.ieee.org/document/11036794/Optimal double-loop graphs datasetundirected double-loop networksfamilies of optimal circulant graphsanalytical descriptions of graph familiesnetworks-on-chip design
spellingShingle Emilia A. Monakhova
Oleg G. Monakhov
Edward R. Rzaev
Aleksandr A. Amerikanov
Mikhail Y. Romashikhin
Aleksandr Y. Romanov
A New Approach for Automatic Search for Families of Optimal Undirected Double-Loop Networks
IEEE Access
Optimal double-loop graphs dataset
undirected double-loop networks
families of optimal circulant graphs
analytical descriptions of graph families
networks-on-chip design
title A New Approach for Automatic Search for Families of Optimal Undirected Double-Loop Networks
title_full A New Approach for Automatic Search for Families of Optimal Undirected Double-Loop Networks
title_fullStr A New Approach for Automatic Search for Families of Optimal Undirected Double-Loop Networks
title_full_unstemmed A New Approach for Automatic Search for Families of Optimal Undirected Double-Loop Networks
title_short A New Approach for Automatic Search for Families of Optimal Undirected Double-Loop Networks
title_sort new approach for automatic search for families of optimal undirected double loop networks
topic Optimal double-loop graphs dataset
undirected double-loop networks
families of optimal circulant graphs
analytical descriptions of graph families
networks-on-chip design
url https://ieeexplore.ieee.org/document/11036794/
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