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Quantum inspired automatic clustering algorithms: A comparative study of genetic algorithm and bat algorithm

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10246920" target="_blank" >RIV/61989100:27240/20:10246920 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.degruyter.com/document/doi/10.1515/9783110670707/html" target="_blank" >https://www.degruyter.com/document/doi/10.1515/9783110670707/html</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1515/9783110670707-005" target="_blank" >10.1515/9783110670707-005</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Quantum inspired automatic clustering algorithms: A comparative study of genetic algorithm and bat algorithm

  • Original language description

    This article is intendant to present two automatic clustering techniques of image datasets, based on quantum inspired framework with two different metaheuristic algorithms, viz., Genetic Algorithm (GA) and Bat Algorithm (BA). This work provides two novel techniques to automatically find out the optimum clusters present in images and also provides a comparative study between the Quantum Inspired Genetic Algorithm (QIGA) and Quantum Inspired Bat Algorithm (QIBA). A comparison is also presented between these quantum inspired algorithms with their analogous classical counterparts. During the experiment, it was perceived that the quantum inspired techniques beat their classical techniques. The comparison was prepared based on the mean values of the fitness, standard deviation, standard error of the computed fitness of the cluster validity index and the optimal computational time. Finally, the supremacy of the algorithms was verified in terms of the p-value which was computed by t-test (statistical superiority test) and ranking of the proposed procedures was produced by the Friedman test. During the computation, the betterment of the fitness was judge by a well-known cluster validity index, named, DB index. The experiments were carried out on four Berkeley image and two real life grey scale images. (C) 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved.

  • Czech name

  • Czech description

Classification

  • Type

    C - Chapter in a specialist book

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2020

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Book/collection name

    Quantum Machine Learning

  • ISBN

    978-3-11-067070-7

  • Number of pages of the result

    26

  • Pages from-to

    89-114

  • Number of pages of the book

    131

  • Publisher name

    De Gruyter

  • Place of publication

    Berlin

  • UT code for WoS chapter