A quantum inspired differential evolution algorithm for automatic clustering of real life datasets

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10254650" target="_blank" >RIV/61989100:27240/24:10254650 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s11042-023-15704-3" target="_blank" >https://link.springer.com/article/10.1007/s11042-023-15704-3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11042-023-15704-3" target="_blank" >10.1007/s11042-023-15704-3</a>

Result language
angličtina
Original language name
A quantum inspired differential evolution algorithm for automatic clustering of real life datasets
Original language description
In recent years, Quantum Inspired Metaheuristic algorithms have emerged to be promising due to their efficiency, robustness and faster computational capability. In this paper, a novel Quantum Inspired Differential Evolution (QIDE) algorithm has been presented for automatic clustering of unlabeled datasets. In case of automatic clustering, the datasets have been clustered into optimal number of groups on the run without any apriori knowledge of the datasets. In this work, the proposed algorithm has been compared with other two quantum inspired algorithms, viz., Fast Quantum Inspired Evolutionary Clustering Algorithm (FQEA) and Quantum Evolutionary Algorithm for Data Clustering (QEAC), a Classical Differential Evolution (CDE) algorithm with different mutation probabilities and an Improved Differential Evolution (IDE) algorithm. The experiments have been conducted on six real life publicly available datasets to identify the optimal number of clusters. By introducing some concepts of quantum gates, the proposed algorithm not only achieves good convergence speed but also provides better results than other competitive algorithms. In addition, Sobol's sensitivity analysis has been conducted for tuning the parameters of the proposed algorithm.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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