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
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Czech description
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Classification
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)
Result continuities
Project
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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