Two-dimensional quantum genetic algorithm: application to task allocation problem

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dc.contributor.authorMondal, Sabyasachi
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2021-03-11T14:53:28Z
dc.date.available2021-03-11T14:53:28Z
dc.date.issued2021-02-10
dc.description.abstractThis 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.en_UK
dc.identifier.citationMondal S, Tsourdos A. (2021) Two-dimensional quantum genetic algorithm: application to task allocation problem. Sensors, Volume 21, Issue 4, February 2021, Article number 1251en_UK
dc.identifier.issn1424-8220
dc.identifier.urihttps://doi.org/10.3390/s21041251
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16469
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjecttask allocationen_UK
dc.subjecttwo-dimensional quantum chromosomeen_UK
dc.subjectQuantum Genetic Algorithmen_UK
dc.titleTwo-dimensional quantum genetic algorithm: application to task allocation problemen_UK
dc.typeArticleen_UK

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