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An efficient hybrid multilayer perceptron neural network with grasshopper optimization

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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.

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Authors and Affiliations

  1. School of Surveying and Geospatial Engineering, University of Tehran, Tehran, Iran

    Ali Asghar Heidari

  2. Business Information Technology Department, King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan

    Hossam Faris & Ibrahim Aljarah

  3. Institute of Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD 4111, Australia

    Seyedali Mirjalili

Authors
  1. Ali Asghar Heidari
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  2. Hossam Faris
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  3. Ibrahim Aljarah
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  4. Seyedali Mirjalili
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Correspondence to Ibrahim Aljarah.

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Heidari, A.A., Faris, H., Aljarah, I. et al. An efficient hybrid multilayer perceptron neural network with grasshopper optimization. Soft Comput 23, 7941–7958 (2019). https://doi.org/10.1007/s00500-018-3424-2

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