An Improved Grey Wolf Optimization-based Learning of Artificial Neural Network for Medical Data Classification

Abstract

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, and machine learning. The different variants of GWO have been developed in the last five years for solving optimization problems in diverse fields. Like other metaheuristic algorithms, GWO also suffers from local optima and slow convergence problems, which result in degraded performance. An adequate equilibrium among exploration and exploitation is a key factor to the success of metaheuristic algorithms, especially for optimization tasks. In this paper, a new variant of GWO, called inertia motivated GWO (IMGWO), was proposed. The aim of IMGWO was to establish better balance between exploration and exploitation. Traditionally, artificial neural network (ANN) with backpropagation (BP) depends on initial values and in turn, attains poor convergence. The metaheuristic approaches are better alternatives instead of BP. The proposed IMGWO was used to train ANN to prove its competency in terms of prediction. The proposed IMGWO-ANN was applied for medical diagnosis tasks. Several benchmark medical datasets including heart disease, breast cancer, hepatitis, and Parkinson's diseases were used for assessing the performance of IMGWO-ANN. The performance measures were described in terms of mean squared errors, classification accuracies, sensitivities, specificities, the area under the curve, and receiver operating characteristic curve. It was found that IMGWO outperformed three popular metaheuristic approaches including GWO, genetic algorithm, and particle swarm optimization. Results confirmed the potency of IMGWO as a viable learning technique for an ANN.