Improved Equilibrium Optimizer for Accurate Training of Feedforward Neural Networks

Abstract

One of the most demanding applications of accurate Artificial Neural Networks (ANN) can be found in medical fields, mainly to make critical decisions. To achieve this goal, an efficient optimization and training method is required to tune the parameters of ANN and to reach the global solutions of these parameters. Equilibrium Optimizer (EO) has recently been introduced to solve optimization problems more reliably than other optimization methods which have the ability to escape from the local optima solutions and to reach the global optimum solution. In this paper, to achieve a higher performance, some modifications are applied to the EO algorithm and the Improved Equilibrium Optimizer (IEO) method is presented which have enough accuracy and reliability to be used in crucial and accurate medical applications. Then, this IEO approach is utilized to learn ANN, and IEO-ANN algorithm will be introduced. The proposed IEO-ANN will be implemented to solve real-world medical problems such as breast cancer detection and heart failure prediction. The obtained results of IEO are compared with those of three other well-known approaches: EO, Particle Swarm Optimizer (PSO), Salp Swarm Optimizer (SSO), and Back Propagation (BP). The recorded results have shown that the proposed IEO algorithm has much higher prediction accuracy than others. Therefore, the presented IEO can improve the accuracy and convergence rate of tuning neural networks, so that the proposed IEO-ANN is a suitable classifying and predicting approach for crucial medical decisions where high accuracy is needed.