An ensemble approach of improved quantum inspired gravitational search algorithm and hybrid deep neural networks for computational optimization

Yogesh Kumar, Shashi Kant Verma () and Sandeep Sharma ()
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Yogesh Kumar: Department of Computer Science & Engineering, Uttarakhand Technical University, Dehradun, Uttarakhand, 248007, India
Shashi Kant Verma: #x2020;Department of Computer Science & Engineering, Govind Ballabh Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand, 246194, India
Sandeep Sharma: #x2021;Centre for Reliability Sciences & Technologies, Department of Electronic Engineering, Chang Gung University, Taoyuan, 33302, Taiwan

International Journal of Modern Physics C (IJMPC), 2021, vol. 32, issue 08, 1-16

Abstract: In this paper, an autonomous ensemble approach of improved quantum inspired gravitational search algorithm (IQI-GSA) and hybrid deep neural networks (HDNN) is proposed for the optimization of computational problems. The IQI-GSA is a combinational variant of gravitational search algorithm (GSA) and quantum computing (QC). The improved variant enhances the diversity of mass collection for retaining the stochastic attributes and handling the local trapping of mass agents. Further, the hybrid deep neural network encompasses the convolutional and recurrent neural networks (HDCR-NN) which analyze the relational & temporal dependencies among the different computational components for optimization. The proposed ensemble approach is evaluated for the application of facial expression recognition by experimentation on Karolinska Directed Emotional Faces (KDEF) and Japanese Female Facial Expression (JAFFE) datasets. The experimentation evaluations evidently exhibit the outperformed recognition rate of the proposed ensemble approach in comparison with state-of-the-art techniques.

Keywords: Gravitational search algorithm; quantum computing; deep neural network; convolutional neural network; recurrent neural network; facial expression recognition (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S012918312150100X

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