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Levy Equilibrium Optimizer algorithm for the DNA storage code set

The generation of massive data puts forward higher requirements for storage technology. DNA storage is a new storage technology which uses biological macromolecule DNA as information carrier. Compared with traditional silicon-based storage, DNA storage has the advantages of large capacity, high dens...

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Detalles Bibliográficos
Autor principal: Zhang, Jianxia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9671426/
https://www.ncbi.nlm.nih.gov/pubmed/36395269
http://dx.doi.org/10.1371/journal.pone.0277139
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author Zhang, Jianxia
author_facet Zhang, Jianxia
author_sort Zhang, Jianxia
collection PubMed
description The generation of massive data puts forward higher requirements for storage technology. DNA storage is a new storage technology which uses biological macromolecule DNA as information carrier. Compared with traditional silicon-based storage, DNA storage has the advantages of large capacity, high density, low energy consumption and high durability. DNA coding is to store data information with as few base sequences as possible without errors. Coding is a key technology in DNA storage, and its results directly affect the performance of storage and the integrity of data reading and writing. In this paper, a Levy Equilibrium Optimizer (LEO) algorithm is proposed to construct a DNA storage code set that satisfies combinatorial constraints. The performance of the proposed algorithm is tested on 13 benchmark functions, and 4 new global optima are obtained. Under the same constraints, the DNA storage code set is constructed. Compared with previous work, the lower bound of DNA storage code set is improved by 4–13%.
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spelling pubmed-96714262022-11-18 Levy Equilibrium Optimizer algorithm for the DNA storage code set Zhang, Jianxia PLoS One Research Article The generation of massive data puts forward higher requirements for storage technology. DNA storage is a new storage technology which uses biological macromolecule DNA as information carrier. Compared with traditional silicon-based storage, DNA storage has the advantages of large capacity, high density, low energy consumption and high durability. DNA coding is to store data information with as few base sequences as possible without errors. Coding is a key technology in DNA storage, and its results directly affect the performance of storage and the integrity of data reading and writing. In this paper, a Levy Equilibrium Optimizer (LEO) algorithm is proposed to construct a DNA storage code set that satisfies combinatorial constraints. The performance of the proposed algorithm is tested on 13 benchmark functions, and 4 new global optima are obtained. Under the same constraints, the DNA storage code set is constructed. Compared with previous work, the lower bound of DNA storage code set is improved by 4–13%. Public Library of Science 2022-11-17 /pmc/articles/PMC9671426/ /pubmed/36395269 http://dx.doi.org/10.1371/journal.pone.0277139 Text en © 2022 Jianxia Zhang 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
Zhang, Jianxia
Levy Equilibrium Optimizer algorithm for the DNA storage code set
title Levy Equilibrium Optimizer algorithm for the DNA storage code set
title_full Levy Equilibrium Optimizer algorithm for the DNA storage code set
title_fullStr Levy Equilibrium Optimizer algorithm for the DNA storage code set
title_full_unstemmed Levy Equilibrium Optimizer algorithm for the DNA storage code set
title_short Levy Equilibrium Optimizer algorithm for the DNA storage code set
title_sort levy equilibrium optimizer algorithm for the dna storage code set
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9671426/
https://www.ncbi.nlm.nih.gov/pubmed/36395269
http://dx.doi.org/10.1371/journal.pone.0277139
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