Improved Hybrid Grey Wolf Optimizer Sine Cosine Algorithm (IHGWOSCA) Trained Artificial Neural Network (ANN) for Classification

The conventional technique for Artificial Neural Network (ANN) training, that is, the Back Propagation algorithm (BP), require large number of iterations and high computational time. The induction of meta-heuristic algorithms improve the training process by introducing randomness into the architecture. However even these algorithms are inclined to fall into the local minima when cost solution search spaces are at greater dimensions and/or convergence rate is slow. To address these inefficiencies in an artificial neural network, the manuscript presents a novel ANN classifier that updates its weights and biases using the simulation of the improved hybrid grey wolf sine cosine algorithm (IHGWOSCA). The proposed network, improved hybrid grey wolf sine cosine algorithm based neural network, consists of an input and output layer and a fully-connected single hidden layer of 10 neurons. Three University of California Irvine (UCI) database datasets were used as basis for the benchmark test namely; ‘Cryotherapy’, ‘Haberman’ and ‘Blood Transfusion Service Center’, training accuracies for which are 93.33%, 74.01% and 76.75% whereas the testing accuracies are 93.33%, 76.47% and 81.52% respectively. Comparative analysis with Group Teaching Optimization Algorithm-Neural Network (GTOANN), Particle Swarm Optimization-Neural Network (PSONN) and Hybrid Grey Wolf Sine Cosine Optimization Algorithm Neural Network (HGWOSCA-NN) show that the proposed IHGWOSCA-NN outperforms in training accuracies as well as in testing accuracies.