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An improved group teaching optimization algorithm for global function optimization
This paper proposes an improved group teaching optimization algorithm (IGTOA) to improve the convergence speed and accuracy of the group teaching optimization algorithm. It assigns teachers independently for each individual, replacing the original way of sharing the same teacher, increasing the evol...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9253333/ https://www.ncbi.nlm.nih.gov/pubmed/35789169 http://dx.doi.org/10.1038/s41598-022-15170-1 |
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author | Wang, Yanjiao Han, Jieru Teng, Ziming |
author_facet | Wang, Yanjiao Han, Jieru Teng, Ziming |
author_sort | Wang, Yanjiao |
collection | PubMed |
description | This paper proposes an improved group teaching optimization algorithm (IGTOA) to improve the convergence speed and accuracy of the group teaching optimization algorithm. It assigns teachers independently for each individual, replacing the original way of sharing the same teacher, increasing the evolutionary direction and expanding the diversity of the population; it dynamically divides the students of the good group and the students of the average group to meet the different needs of convergence speed and population diversity in different evolutionary stages; in the student learning stage, the weak self-learning part is canceled, the mutual learning part is increased, and the population diversity is supplemented; for the average group students, a new sub-space search mode is proposed, and the teacher's teaching method is improved to reduce the diversity in the population evolution process. and propose a population reconstruction mechanism to expand the search range of the current population and ensure population diversity. Finally, the experimental results on the CEC2013 test suite show that IGTOA has clear advantages in convergence speed and accuracy over the other five excellent algorithms. |
format | Online Article Text |
id | pubmed-9253333 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-92533332022-07-06 An improved group teaching optimization algorithm for global function optimization Wang, Yanjiao Han, Jieru Teng, Ziming Sci Rep Article This paper proposes an improved group teaching optimization algorithm (IGTOA) to improve the convergence speed and accuracy of the group teaching optimization algorithm. It assigns teachers independently for each individual, replacing the original way of sharing the same teacher, increasing the evolutionary direction and expanding the diversity of the population; it dynamically divides the students of the good group and the students of the average group to meet the different needs of convergence speed and population diversity in different evolutionary stages; in the student learning stage, the weak self-learning part is canceled, the mutual learning part is increased, and the population diversity is supplemented; for the average group students, a new sub-space search mode is proposed, and the teacher's teaching method is improved to reduce the diversity in the population evolution process. and propose a population reconstruction mechanism to expand the search range of the current population and ensure population diversity. Finally, the experimental results on the CEC2013 test suite show that IGTOA has clear advantages in convergence speed and accuracy over the other five excellent algorithms. Nature Publishing Group UK 2022-07-04 /pmc/articles/PMC9253333/ /pubmed/35789169 http://dx.doi.org/10.1038/s41598-022-15170-1 Text en © The Author(s) 2022 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 Wang, Yanjiao Han, Jieru Teng, Ziming An improved group teaching optimization algorithm for global function optimization |
title | An improved group teaching optimization algorithm for global function optimization |
title_full | An improved group teaching optimization algorithm for global function optimization |
title_fullStr | An improved group teaching optimization algorithm for global function optimization |
title_full_unstemmed | An improved group teaching optimization algorithm for global function optimization |
title_short | An improved group teaching optimization algorithm for global function optimization |
title_sort | improved group teaching optimization algorithm for global function optimization |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9253333/ https://www.ncbi.nlm.nih.gov/pubmed/35789169 http://dx.doi.org/10.1038/s41598-022-15170-1 |
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