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Artificial Neural Networks Hidden Unit and Weight Connection Optimization by Quasi-Refection-Based Learning Artificial Bee Colony Algorithm

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F21%3A50019006" target="_blank" >RIV/62690094:18470/21:50019006 - isvavai.cz</a>

  • Result on the web

    <a href="https://ieeexplore.ieee.org/document/9648205" target="_blank" >https://ieeexplore.ieee.org/document/9648205</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ACCESS.2021.3135201" target="_blank" >10.1109/ACCESS.2021.3135201</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Artificial Neural Networks Hidden Unit and Weight Connection Optimization by Quasi-Refection-Based Learning Artificial Bee Colony Algorithm

  • Original language description

    Artificial neural networks are one of the most commonly used methods in machine learning. Performance of network highly depends on the learning method. Traditional learning algorithms are prone to be trapped in local optima and have slow convergence. At the other hand, nature-inspired optimization algorithms are proven to be very efficient in complex optimization problems solving due to derivative-free solutions. Addressing issues of traditional learning algorithms, in this study, an enhanced version of artificial bee colony nature-inspired metaheuristics is proposed to optimize connection weights and hidden units of artificial neural networks. Proposed improved method incorporates quasi-reflection-based learning and guided best solution bounded mechanisms in the original approach and manages to conquer its deficiencies. First, the method is tested on a recent challenging CEC 2017 benchmark function set, then applied for training artificial neural network on five well-known medical benchmark datasets. Further, devised algorithm is compared to other metaheuristics-based methods. The efficiency is measured by five metrics-accuracy, specificity, sensitivity, geometric mean, and area under the curve. Simulation results prove that the proposed algorithm outperforms other metaheuristics in terms of accuracy and convergence speed. The improvement of the accuracy over the other methods on different datasets are between 0.03% and 12.94%. The quasi-refection-based learning mechanism significantly improves the convergence speed of the original artificial bee colony algorithm and together with the guided best solution bounded, the exploitation capability is enhanced, which results in significantly better accuracy.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • 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

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2021

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    IEEE Access

  • ISSN

    2169-3536

  • e-ISSN

    2169-3536

  • Volume of the periodical

    9

  • Issue of the periodical within the volume

    December

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    21

  • Pages from-to

    169135-169155

  • UT code for WoS article

    000736731200001

  • EID of the result in the Scopus database

    2-s2.0-85121844511