An efficient hybrid multilayer perceptron neural network with grasshopper optimization

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

Research output: Contribution to journalArticlepeer-review

199 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)7941-7958
Number of pages18
JournalSoft Computing
Issue number17
Publication statusPublished - 1 Sept 2019
Externally publishedYes


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


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