An efficient hybrid multilayer perceptron neural network with grasshopper optimization

• Published in: Soft computing (Berlin, Germany) Vol. 23; no. 17; pp. 7941 - 7958
• Main Authors: Heidari, Ali Asghar, Faris, Hossam, Aljarah, Ibrahim, Mirjalili, Seyedali
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2019; Springer Nature B.V
• Subjects: 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; Grasshopper Optimization Algorithm; Multilayer perceptron; Medical diagnosis; Optimization; Classification
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-018-3424-2

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.