An efficient hybrid multilayer perceptron neural network with grasshopper optimization

作者：Heidari, Ali Asghar; Faris, Hossam; Aljarah, Ibrahim^*; Mirjalili, Seyedali

来源：Soft Computing, 2019, 23(17): 7941-7958.

DOI：10.1007/s00500-018-3424-2

• Optimization
• Classification
• Grasshopper Optimization Algorithm
• Multilayer perceptron
• Medical diagnosis

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.

• 出版日期2019-9