Adaptive tTrainer for Multi-layer Perceptron using African Vultures Optimization Algorithm

Abstract

This paper utilized a newly proposed multi-layer perceptron (MLP) that has been trained using a meta-heuristic technique (algorithm) that was developed using the idea of the African Vultures Optimization Algorithm. The precision and consistency of the proposed method's convergence as performance metrics. The African Vultures Optimization Algorithm(AVOA) was recently proposed for use in training multi-layer perceptron (MLP), and it employs the five most common classification data sets currently available( XOR, balloon, breast cancer, heart, Iris) in the California University at Irvine UCI Repository .The newly Optimizers (AVOA) are being us for the first time as a Multi-Layer Perceptron (MLP) trainer, and its results are compared to those obtained using the more established gray wolf optimization (GWO), the whale optimization algorithm (WOA), and the sine cosine algorithm are examples of optimization techniques (SCA). Previously, AVOA was used to determine the best weights and biases for the optimal solution.