Adaptive hyperparameter optimization in artificial neural networks using the bobcat optimization algorithm (BOA)

Hyperparameter selection plays a critical role in the convergence speed and performance of artificial neural networks (ANNs), yet conventional tuning methods such as grid search, random search, and Bayesian optimization often suffer from high computational cost and limited adaptability. To address these limitations, this study proposes an adaptive hyperparameter optimization framework based on the Bobcat Optimization Algorithm (BOA) for tuning the learning rate and momentum of an ANN. BOA employs a biologically inspired exploration-exploitation mechanism that dynamically adjusts hyperparameters according to training performance, enabling efficient search without relying on probabilistic surrogate models. The proposed BOA-ANN framework is evaluated on the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset using a three-layer feedforward neural network. Experimental results demonstrate that BOA-ANN achieves a test accuracy of 98.52, a mean squared error of 0.014, and an F1-score of 98.30, outperforming Bayesian optimization, the Secretary Algorithm, and Automated Model Compression (AMC) pruning under identical settings.