Optimization of convolutional neural network topology and training parameters using Bees Algorithm

Alamri, Nawaf Mohammad H ORCID: https://orcid.org/0000-0002-5641-0178, Packianather, Michael ORCID: https://orcid.org/0000-0002-9436-8206 and Bigot, Samuel ORCID: https://orcid.org/0000-0002-0789-4727 2022. Optimization of convolutional neural network topology and training parameters using Bees Algorithm. Presented at: IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC), Gunupur, Odisha, India, 15-17 December 2022. Proceedings of 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security. IEEE, pp. 1-6. 10.1109/iSSSC56467.2022.10051487

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Abstract

Designing a Convolutional Neural Network (CNN) topology with optimal performance is a challenge. This paper proposes a hybrid algorithm combining the nature-inspired Bees Algorithm (BA) with Bayesian Optimization (BO) technique to improve CNN performance (BA-BO-CNN). In addition, another hybrid algorithm is proposed which uses BA to optimize CNN hyperparameters (BA-CNN) to improve the network performance. Applying the hybrid BA-BO-CNN rather than BA-CNN on human electrocardiogram (ECG) signals the testing accuracy improved from 92.50% to 95%, on Cifar10DataDir benchmark data the accuracy on the validation set increased from 80.72% for BO-CNN to 82.22% for BA-BO-CNN, and finally, on benchmark digits images the training, validation and testing accuracies remained the same compared to the existing BO-CNN, but with more efficient computational time since it is reduced by 3 minutes and 12 seconds for BA-BO-CNN and 4 minutes and 14 seconds for BA-CNN.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	IEEE
ISBN:	9781665490573
Last Modified:	05 Apr 2023 11:45
URI:	https://orca.cardiff.ac.uk/id/eprint/158386

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