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2025 | OriginalPaper | Chapter

Optimisation of Convolutional Neural Network Parameters Using the Bees Algorithm

Authors : Michael S. Packianather, Nawaf Mohammad H. Alamri

Published in: Intelligent Engineering Optimisation with the Bees Algorithm

Publisher: Springer Nature Switzerland

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Abstract

The convolutional neural network (CNN) is one of the most popular deep learning algorithms that deals mainly with image data. In this study, the application of the Bees Algorithm (BA), which behaves like honeybees, was used along with the Bayesian Optimisation (BO) approach to improve CNN performance (BA-BO-CNN). Applying the hybrid algorithm on Cifar10DataDir images increased the accuracy on the validation set from 80.72% for the existing BO-CNN to 82.22% for the hybrid algorithm. Applying BA-BO-CNN on digit datasets showed the same accuracy as the existing hybrid BO-CNN, but with a computational time shortened by 3 min and 12 s. Finally, concrete crack benchmark image data yielded almost similar results to existing algorithms. Similarly, a new hybrid Bees Convolutional Neural Network (BA-CNN) algorithm was proposed that uses BA to design better CNN topology by optimising its hyperparameters, which increases the network accuracy. Applying the hybrid algorithm to the Cifar10DataDir dataset yielded the same accuracy as existing CNNs and BO-CNNs. Applying it to the digits dataset produced the lowest computational time with 4 min and 14 s reductions compared to BO-CNN, so it is the best algorithm in terms of cost-effectiveness. Finally, applying it to concrete crack images produced similar results to the existing algorithms.

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Metadata
Title
Optimisation of Convolutional Neural Network Parameters Using the Bees Algorithm
Authors
Michael S. Packianather
Nawaf Mohammad H. Alamri
Copyright Year
2025
DOI
https://doi.org/10.1007/978-3-031-64936-3_13

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