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A module classification method for light industrial equipment based on improved NSGA2-FCM algorithm

In response to the problem that it is easy to fall into local optimum when using the traditional clustering algorithm to divide the modules, this paper improves the initialisation strategy of the NSGA2 algorithm and combines it with the FCM algorithm to propose an improved NSGA2-FCM algorithm for cl...

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Autores principales: Zheng, Hui, Guo, Hanwen, Pang, Tonglin, Guo, Zijian, Guo, Xiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10447435/
https://www.ncbi.nlm.nih.gov/pubmed/37612438
http://dx.doi.org/10.1038/s41598-023-39116-3
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author Zheng, Hui
Guo, Hanwen
Pang, Tonglin
Guo, Zijian
Guo, Xiao
author_facet Zheng, Hui
Guo, Hanwen
Pang, Tonglin
Guo, Zijian
Guo, Xiao
author_sort Zheng, Hui
collection PubMed
description In response to the problem that it is easy to fall into local optimum when using the traditional clustering algorithm to divide the modules, this paper improves the initialisation strategy of the NSGA2 algorithm and combines it with the FCM algorithm to propose an improved NSGA2-FCM algorithm for clustering analysis. Firstly, the FBS mapping is used to model the functional structure of the product system and identify the relationship between the product functional structures. Secondly, a correlation synthesis matrix is constructed based on the relationships between the module division drivers. Finally, the improved NSGA2-FCM algorithm is applied to cluster analysis of the product to derive the best module division scheme. The algorithm avoids falling into local optima by optimising the initialisation strategy of the NSGA2 algorithm, while using the FCM algorithm to improve the accuracy of the clustering. This allows the algorithm to explore the solution space more effectively when finding the best module partitioning solution. Finally, the effectiveness of the algorithm for module classification of light industrial equipment is verified using beer fermenters as a case study.
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spelling pubmed-104474352023-08-25 A module classification method for light industrial equipment based on improved NSGA2-FCM algorithm Zheng, Hui Guo, Hanwen Pang, Tonglin Guo, Zijian Guo, Xiao Sci Rep Article In response to the problem that it is easy to fall into local optimum when using the traditional clustering algorithm to divide the modules, this paper improves the initialisation strategy of the NSGA2 algorithm and combines it with the FCM algorithm to propose an improved NSGA2-FCM algorithm for clustering analysis. Firstly, the FBS mapping is used to model the functional structure of the product system and identify the relationship between the product functional structures. Secondly, a correlation synthesis matrix is constructed based on the relationships between the module division drivers. Finally, the improved NSGA2-FCM algorithm is applied to cluster analysis of the product to derive the best module division scheme. The algorithm avoids falling into local optima by optimising the initialisation strategy of the NSGA2 algorithm, while using the FCM algorithm to improve the accuracy of the clustering. This allows the algorithm to explore the solution space more effectively when finding the best module partitioning solution. Finally, the effectiveness of the algorithm for module classification of light industrial equipment is verified using beer fermenters as a case study. Nature Publishing Group UK 2023-08-23 /pmc/articles/PMC10447435/ /pubmed/37612438 http://dx.doi.org/10.1038/s41598-023-39116-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Zheng, Hui
Guo, Hanwen
Pang, Tonglin
Guo, Zijian
Guo, Xiao
A module classification method for light industrial equipment based on improved NSGA2-FCM algorithm
title A module classification method for light industrial equipment based on improved NSGA2-FCM algorithm
title_full A module classification method for light industrial equipment based on improved NSGA2-FCM algorithm
title_fullStr A module classification method for light industrial equipment based on improved NSGA2-FCM algorithm
title_full_unstemmed A module classification method for light industrial equipment based on improved NSGA2-FCM algorithm
title_short A module classification method for light industrial equipment based on improved NSGA2-FCM algorithm
title_sort module classification method for light industrial equipment based on improved nsga2-fcm algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10447435/
https://www.ncbi.nlm.nih.gov/pubmed/37612438
http://dx.doi.org/10.1038/s41598-023-39116-3
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