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Hyperparameters optimization of neural network using improved particle swarm optimization for modeling of electromagnetic inverse problems

Published online by Cambridge University Press:  17 December 2021

Debanjali Sarkar ,
Taimoor Khan Open the ORCID record for Taimoor Khan [Opens in a new window]  and
Fazal Ahmed Talukdar
Debanjali Sarkar
Affiliation:
Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, India
Taimoor Khan*
Affiliation:
Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, India
Fazal Ahmed Talukdar
Affiliation:
Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, India
*
Author for correspondence: Taimoor Khan, E-mail: ktaimoor@ieee.org
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Abstract

Optimization of hyperparameters of artificial neural network (ANN) usually involves a trial and error approach which is not only computationally expensive but also fails to predict a near-optimal solution most of the time. To design a better optimized ANN model, evolutionary algorithms are widely utilized to determine hyperparameters. This work proposes hyperparameters optimization of the ANN model using an improved particle swarm optimization (IPSO) algorithm. The different ANN hyperparameters considered are a number of hidden layers, neurons in each hidden layer, activation function, and training function. The proposed technique is validated using inverse modeling of two meander line electromagnetic bandgap unit cells and a slotted ultra-wideband antenna loaded with EBG structures. Three other evolutionary algorithms viz. hybrid PSO, conventional PSO, and genetic algorithm are also adopted for the hyperparameter optimization of the ANN models for comparative analysis. Performances of all the models are evaluated using quantitative assessment parameters viz. mean square error, mean absolute percentage deviation, and coefficient of determination (R2). The comparative investigation establishes the accurate and efficient prediction capability of the ANN models tuned using IPSO compared to other evolutionary algorithms.

Keywords

Artificial neural network (ANN)electromagnetic bandgap (EBG)genetic algorithm (GA)monopole antennaparticle swarm optimization (PSO)ultra-wideband (UWB)
Type
Antenna Design, Modelling and Measurements
Information
International Journal of Microwave and Wireless Technologies , Volume 14 , Issue 10 , December 2022 , pp. 1326 - 1337
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press in association with the European Microwave Association

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