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QPSO-Based Adaptive DNA Computing Algorithm
DNA (deoxyribonucleic acid) computing that is a new computation model based on DNA molecules for information storage has been increasingly used for optimization and data analysis in recent years. However, DNA computing algorithm has some limitations in terms of convergence speed, adaptability, and e...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3727123/ https://www.ncbi.nlm.nih.gov/pubmed/23935409 http://dx.doi.org/10.1155/2013/160687 |
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author | Karakose, Mehmet Cigdem, Ugur |
author_facet | Karakose, Mehmet Cigdem, Ugur |
author_sort | Karakose, Mehmet |
collection | PubMed |
description | DNA (deoxyribonucleic acid) computing that is a new computation model based on DNA molecules for information storage has been increasingly used for optimization and data analysis in recent years. However, DNA computing algorithm has some limitations in terms of convergence speed, adaptability, and effectiveness. In this paper, a new approach for improvement of DNA computing is proposed. This new approach aims to perform DNA computing algorithm with adaptive parameters towards the desired goal using quantum-behaved particle swarm optimization (QPSO). Some contributions provided by the proposed QPSO based on adaptive DNA computing algorithm are as follows: (1) parameters of population size, crossover rate, maximum number of operations, enzyme and virus mutation rate, and fitness function of DNA computing algorithm are simultaneously tuned for adaptive process, (2) adaptive algorithm is performed using QPSO algorithm for goal-driven progress, faster operation, and flexibility in data, and (3) numerical realization of DNA computing algorithm with proposed approach is implemented in system identification. Two experiments with different systems were carried out to evaluate the performance of the proposed approach with comparative results. Experimental results obtained with Matlab and FPGA demonstrate ability to provide effective optimization, considerable convergence speed, and high accuracy according to DNA computing algorithm. |
format | Online Article Text |
id | pubmed-3727123 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-37271232013-08-09 QPSO-Based Adaptive DNA Computing Algorithm Karakose, Mehmet Cigdem, Ugur ScientificWorldJournal Research Article DNA (deoxyribonucleic acid) computing that is a new computation model based on DNA molecules for information storage has been increasingly used for optimization and data analysis in recent years. However, DNA computing algorithm has some limitations in terms of convergence speed, adaptability, and effectiveness. In this paper, a new approach for improvement of DNA computing is proposed. This new approach aims to perform DNA computing algorithm with adaptive parameters towards the desired goal using quantum-behaved particle swarm optimization (QPSO). Some contributions provided by the proposed QPSO based on adaptive DNA computing algorithm are as follows: (1) parameters of population size, crossover rate, maximum number of operations, enzyme and virus mutation rate, and fitness function of DNA computing algorithm are simultaneously tuned for adaptive process, (2) adaptive algorithm is performed using QPSO algorithm for goal-driven progress, faster operation, and flexibility in data, and (3) numerical realization of DNA computing algorithm with proposed approach is implemented in system identification. Two experiments with different systems were carried out to evaluate the performance of the proposed approach with comparative results. Experimental results obtained with Matlab and FPGA demonstrate ability to provide effective optimization, considerable convergence speed, and high accuracy according to DNA computing algorithm. Hindawi Publishing Corporation 2013-07-15 /pmc/articles/PMC3727123/ /pubmed/23935409 http://dx.doi.org/10.1155/2013/160687 Text en Copyright © 2013 M. Karakose and U. Cigdem. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Karakose, Mehmet Cigdem, Ugur QPSO-Based Adaptive DNA Computing Algorithm |
title | QPSO-Based Adaptive DNA Computing Algorithm |
title_full | QPSO-Based Adaptive DNA Computing Algorithm |
title_fullStr | QPSO-Based Adaptive DNA Computing Algorithm |
title_full_unstemmed | QPSO-Based Adaptive DNA Computing Algorithm |
title_short | QPSO-Based Adaptive DNA Computing Algorithm |
title_sort | qpso-based adaptive dna computing algorithm |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3727123/ https://www.ncbi.nlm.nih.gov/pubmed/23935409 http://dx.doi.org/10.1155/2013/160687 |
work_keys_str_mv | AT karakosemehmet qpsobasedadaptivednacomputingalgorithm AT cigdemugur qpsobasedadaptivednacomputingalgorithm |