Fox Artificial Neural Network: FOXANN is a novel classification model that combines the recently developed Fox optimizer with ANN to solve ML problems. Fox optimizer replaces the backpropagation algorithm in ANN; optimizes synaptic weights; and achieves high classification accuracy with a minimum loss, improved model generalization, and interpretability. The performance of FOXANN is evaluated on three standard datasets: Iris Flower, Breast Cancer Wisconsin, and Wine. Moreover, the results show that FOXANN outperforms traditional ANN and logistic regression methods as well as other models proposed in the literature such as ABC-ANN,
ABC-MNN, CROANN, and PSO-DNN, achieving a higher accuracy of 0.9969 and a lower validation loss of 0.0028. These results demonstrate that FOXANN is more effective than traditional methods and other proposed models across standard datasets. Thus, FOXANN effectively addresses the challenges in ML algorithms and improves classification performance.
Fitness Dependent Optimizer (FDO) algorithm for training a Multilayer Perceptron Neural Network (MLP)
Child Drawing Development Optimization Algorithm Based on Child’s Cognitive Development
LSTM trained with two optimizing algorithms. The optimization algorithms are biogeography-based optimization (BBO) and genetic algorithm (GA)
Using Accuracy Measure for Improving the Training of LSTM with Metaheuristic Algorithms; Harmony Search, Gray Wolf Optimizer, Sine Cosine, and Ant Lion Optimization algorithms.
A hybrid system (a modified Recurrent Neural Network with a modified Grey Wolf Optimizer)