Identifying the impact of Metaperceptron in optimizing neural networks: a comparative study of gradient descent and metaheuristic approaches - Darwin, Darwin; Harumy, Tengku Henny Febriana; Efendi, Syahril; Juliandy, Carles; Halim, Binarwan (Eastern-European Journal of Enterprise Technologies, 2025)
This study investigates the application of the Metaperceptron framework as an adaptive optimization mechanism in training neural networks for polycystic ovary syndrome (PCOS) diagnosis. The research addresses the persistent challenges in conventional optimization methods such as slow convergence, local minima entrapment, and hyperparameter sensitivity that hinder the efficiency and generalization capability of artificial neural networks. By integrating Metaperceptron with both gradient descent (GD) and genetic algorithm (GA), this work demonstrates significant improvements in convergence speed and diagnostic accuracy. Specifically, Metaperceptron-enhanced GD reduced convergence time by nearly 40% while maintaining high accuracy (0.8950 for single-layer neural network and 0.9100 for multi-layer neural network). These results were achieved through dynamic learning rate adjustment and meta-level control over search strategies, enabling better exploration-exploitation balance during training. The findings are explained by the framework’s ability to adaptively respond to gradient landscapes and dataset characteristics, offering a more stable and efficient optimization process. Practical implementation of the proposed method is feasible under conditions where data quality and representativeness are ensured, particularly in medical diagnostics and other domains involving imbalanced or noisy datasets