An effective gbest-guided gravitational search algorithm for real-parameter optimization and its application in training of feedforward neural networks

2018 ◽  
Vol 143 ◽  
pp. 192-207 ◽  
Author(s):  
Vijay Kumar Bohat ◽  
K.V. Arya
Keyword(s):  
Neural Networks ◽  
Parameter Optimization ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Feedforward Neural Networks ◽  
Real Parameter ◽  
Gravitational Search

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

2021 ◽  
pp. 2150100
Author(s):  
Yogesh Kumar ◽  
Shashi Kant Verma ◽  
Sandeep Sharma
Keyword(s):  
Neural Networks ◽  
Facial Expression ◽  
Deep Neural Networks ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Recognition Rate ◽  
Expression Recognition ◽  
Computational Optimization ◽  
Ensemble Approach ◽  
Gravitational Search

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.

Sign in / Sign up

Export Citation Format

Share Document