Two-Dimensional Quantum Genetic Algorithm: Application to Task Allocation Problem

This paper presents a Two-Dimensional Quantum Genetic Algorithm (2D-QGA), which is a new variety of QGA. This variety will allow the user to take the advantages of quantum computation while solving the problems which are suitable for two-dimensional (2D) representation or can be represented in tabul...

Descripción completa

Detalles Bibliográficos
Autores principales: Mondal, Sabyasachi, Tsourdos, Antonios
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7916501/
https://www.ncbi.nlm.nih.gov/pubmed/33578712
http://dx.doi.org/10.3390/s21041251
_version_ 1783657491559612416
author Mondal, Sabyasachi
Tsourdos, Antonios
author_facet Mondal, Sabyasachi
Tsourdos, Antonios
author_sort Mondal, Sabyasachi
collection PubMed
description This paper presents a Two-Dimensional Quantum Genetic Algorithm (2D-QGA), which is a new variety of QGA. This variety will allow the user to take the advantages of quantum computation while solving the problems which are suitable for two-dimensional (2D) representation or can be represented in tabular form. The performance of 2D-QGA is compared to two-dimensional GA (2D-GA), which is used to solve two-dimensional problems as well. The comparison study is performed by applying both the algorithm to the task allocation problem. The performance of 2D-QGA is better than 2D-GA while comparing execution time, convergence iteration, minimum cost generated, and population size.
format Online
Article
Text
id pubmed-7916501
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-79165012021-03-01 Two-Dimensional Quantum Genetic Algorithm: Application to Task Allocation Problem Mondal, Sabyasachi Tsourdos, Antonios Sensors (Basel) Article This paper presents a Two-Dimensional Quantum Genetic Algorithm (2D-QGA), which is a new variety of QGA. This variety will allow the user to take the advantages of quantum computation while solving the problems which are suitable for two-dimensional (2D) representation or can be represented in tabular form. The performance of 2D-QGA is compared to two-dimensional GA (2D-GA), which is used to solve two-dimensional problems as well. The comparison study is performed by applying both the algorithm to the task allocation problem. The performance of 2D-QGA is better than 2D-GA while comparing execution time, convergence iteration, minimum cost generated, and population size. MDPI 2021-02-10 /pmc/articles/PMC7916501/ /pubmed/33578712 http://dx.doi.org/10.3390/s21041251 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mondal, Sabyasachi
Tsourdos, Antonios
Two-Dimensional Quantum Genetic Algorithm: Application to Task Allocation Problem
title Two-Dimensional Quantum Genetic Algorithm: Application to Task Allocation Problem
title_full Two-Dimensional Quantum Genetic Algorithm: Application to Task Allocation Problem
title_fullStr Two-Dimensional Quantum Genetic Algorithm: Application to Task Allocation Problem
title_full_unstemmed Two-Dimensional Quantum Genetic Algorithm: Application to Task Allocation Problem
title_short Two-Dimensional Quantum Genetic Algorithm: Application to Task Allocation Problem
title_sort two-dimensional quantum genetic algorithm: application to task allocation problem
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7916501/
https://www.ncbi.nlm.nih.gov/pubmed/33578712
http://dx.doi.org/10.3390/s21041251
work_keys_str_mv AT mondalsabyasachi twodimensionalquantumgeneticalgorithmapplicationtotaskallocationproblem
AT tsourdosantonios twodimensionalquantumgeneticalgorithmapplicationtotaskallocationproblem

Ejemplares similares