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Multi-membrane search algorithm
This research proposes a new multi-membrane search algorithm (MSA) based on cell biological behavior. Cell secretion protein behavior and cell division and fusion strategy are the main inspirations for the algorithm. In order to verify the performance of the algorithm, we used 19 benchmark functions...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8648127/ https://www.ncbi.nlm.nih.gov/pubmed/34871309 http://dx.doi.org/10.1371/journal.pone.0260512 |
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author | Song, Qi Huang, Yourui Lai, Wenhao Han, Tao XU, Shanyong Rong, Xue |
author_facet | Song, Qi Huang, Yourui Lai, Wenhao Han, Tao XU, Shanyong Rong, Xue |
author_sort | Song, Qi |
collection | PubMed |
description | This research proposes a new multi-membrane search algorithm (MSA) based on cell biological behavior. Cell secretion protein behavior and cell division and fusion strategy are the main inspirations for the algorithm. In order to verify the performance of the algorithm, we used 19 benchmark functions to compare the MSA test results with MVO, GWO, MFO and ALO. The number of iterations of each algorithm on each benchmark function is 100, the population number is 10, and the running is repeated 50 times, and the average and standard deviation of the results are recorded. Tests show that the MSA is competitive in unimodal benchmark functions and multi-modal benchmark functions, and the results in composite benchmark functions are all superior to MVO, MFO, ALO, and GWO algorithms. This paper also uses MSA to solve two classic engineering problems: welded beam design and pressure vessel design. The result of welded beam design is 1.7252, and the result of pressure vessel design is 5887.7052, which is better than other comparison algorithms. Statistical experiments show that MSA is a high-performance algorithm that is competitive in unimodal and multimodal functions, and its performance in compound functions is significantly better than MVO, MFO, ALO, and GWO algorithms. |
format | Online Article Text |
id | pubmed-8648127 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-86481272021-12-07 Multi-membrane search algorithm Song, Qi Huang, Yourui Lai, Wenhao Han, Tao XU, Shanyong Rong, Xue PLoS One Research Article This research proposes a new multi-membrane search algorithm (MSA) based on cell biological behavior. Cell secretion protein behavior and cell division and fusion strategy are the main inspirations for the algorithm. In order to verify the performance of the algorithm, we used 19 benchmark functions to compare the MSA test results with MVO, GWO, MFO and ALO. The number of iterations of each algorithm on each benchmark function is 100, the population number is 10, and the running is repeated 50 times, and the average and standard deviation of the results are recorded. Tests show that the MSA is competitive in unimodal benchmark functions and multi-modal benchmark functions, and the results in composite benchmark functions are all superior to MVO, MFO, ALO, and GWO algorithms. This paper also uses MSA to solve two classic engineering problems: welded beam design and pressure vessel design. The result of welded beam design is 1.7252, and the result of pressure vessel design is 5887.7052, which is better than other comparison algorithms. Statistical experiments show that MSA is a high-performance algorithm that is competitive in unimodal and multimodal functions, and its performance in compound functions is significantly better than MVO, MFO, ALO, and GWO algorithms. Public Library of Science 2021-12-06 /pmc/articles/PMC8648127/ /pubmed/34871309 http://dx.doi.org/10.1371/journal.pone.0260512 Text en © 2021 Song et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Song, Qi Huang, Yourui Lai, Wenhao Han, Tao XU, Shanyong Rong, Xue Multi-membrane search algorithm |
title | Multi-membrane search algorithm |
title_full | Multi-membrane search algorithm |
title_fullStr | Multi-membrane search algorithm |
title_full_unstemmed | Multi-membrane search algorithm |
title_short | Multi-membrane search algorithm |
title_sort | multi-membrane search algorithm |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8648127/ https://www.ncbi.nlm.nih.gov/pubmed/34871309 http://dx.doi.org/10.1371/journal.pone.0260512 |
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