AN IMPROVED GREY WOLF OPTIMIZATION-BASED LEARNING OF ARTIFICIAL NEURAL NETWORK FOR MEDICAL DATA CLASSIFICATION

Grey wolf optimization (GWO) is a recent and popular swarm-based metaheuristic approach. It has been used in numerous fields such as numerical optimization, engineering problems, machine learning. The different variants of GWO have developed the last 5 years for solving problems diverse fields. Like other algorithms, also suffers from local optima slow convergence resulted degraded performance. An adequate equilibrium among exploration exploitation key factor to success meta-heuristic algorithms especially task. In this paper, new variant GWO, called inertia motivated (IMGWO) proposed. aim IMGWO establish better balance between exploitation. Traditionally, artificial neural network (ANN) with backpropagation (BP) depends on initial values turn, attains poor convergence. approaches are alternative instead BP. proposed train ANN prove its competency terms prediction. IMGWO-ANN medical diagnosis Some benchmark datasets including heart disease, breast cancer, hepatitis, parkinson's diseases assessing performance IMGWO-ANN. measures described mean squared errors (MSEs), classification accuracies, sensitivities, specificities, area under curve (AUC), receiver operating characteristic (ROC) curve. found that outperforms than three genetic algorithm (GA), particle swarm (PSO). Results confirmed potency viable learning technique an ANN.