Journal Article

An efficient hybrid multilayer perceptron neural network with grasshopper optimization

Heidari, Ali Asghar, Faris, Hossam, Aljarah, Ibrahim, Mirjalili, Seyedali

2019

Overview

This paper proposes a new hybrid stochastic training algorithm using the recently proposed grasshopper optimization algorithm (GOA) for multilayer perceptrons (MLPs) neural networks. The GOA algorithm is an emerging technique with a high potential in tackling optimization problems based on its flexible and adaptive searching mechanisms. It can demonstrate a satisfactory performance by escaping from local optima and balancing the exploration and exploitation trends. The proposed GOAMLP model is then applied to five important datasets: breast cancer, parkinson, diabetes, coronary heart disease, and orthopedic patients. The results are deeply validated in comparison with eight recent and well-regarded algorithms qualitatively and quantitatively. It is shown and proved that the proposed stochastic training algorithm GOAMLP is substantially beneficial in improving the classification rate of MLPs.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Algorithms / Artificial Intelligence / Back propagation / Computational Intelligence / Control / Diabetes / Engineering / Grasshoppers / Heart diseases / Machine learning / Mathematical Logic and Foundations / Mechatronics / Methodologies and Application / Multilayer perceptrons / Neural networks / Optimization / Optimization algorithms / Orthopedics / Propagation / Robotics / Training