Two-dimensional quantum genetic algorithm: application to task allocation problem
| dc.contributor.author | Mondal, Sabyasachi | |
| dc.contributor.author | Tsourdos, Antonios | |
| dc.date.accessioned | 2021-03-11T14:53:28Z | |
| dc.date.available | 2021-03-11T14:53:28Z | |
| dc.date.issued | 2021-02-10 | |
| dc.description.abstract | 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. | en_UK |
| dc.identifier.citation | Mondal S, Tsourdos A. (2021) Two-dimensional quantum genetic algorithm: application to task allocation problem. Sensors, Volume 21, Issue 4, February 2021, Article number 1251 | en_UK |
| dc.identifier.issn | 1424-8220 | |
| dc.identifier.uri | https://doi.org/10.3390/s21041251 | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/16469 | |
| dc.language.iso | en | en_UK |
| dc.publisher | MDPI | en_UK |
| dc.rights | Attribution 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | task allocation | en_UK |
| dc.subject | two-dimensional quantum chromosome | en_UK |
| dc.subject | Quantum Genetic Algorithm | en_UK |
| dc.title | Two-dimensional quantum genetic algorithm: application to task allocation problem | en_UK |
| dc.type | Article | en_UK |