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Soft Computing
Erschienen in: Soft Computing 17/2019

30.07.2018 | Methodologies and Application

An efficient hybrid multilayer perceptron neural network with grasshopper optimization

Erschienen in: Soft Computing | Ausgabe 17/2019

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Abstract

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.
Vorheriger Artikel Hybrid evolutionary programming using adaptive Lévy mutation and modified Nelder–Mead method
Nächster Artikel A semianalytical method for fuzzy integro-differential equations under generalized Seikkala derivative

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Metadaten
Titel
An efficient hybrid multilayer perceptron neural network with grasshopper optimization
Publikationsdatum
30.07.2018
Erschienen in
Soft Computing / Ausgabe 17/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-018-3424-2

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