Study of an Improved Quantum Particle Swarm Optimization-Based Framework for Neural Network Optimization in Modelling of Polymer Data

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Year : 2026 | Volume : 14 | 04 | Page :
    By

    Kakali Das,

  • Amrita Mitra,

  • Sagardeep Ghosh,

  • Suchismita Maiti,

  • Soumyabrata Saha,

  • Pushpita Roy,

  • Anubrata Mondal,

  • Pijush Dutta,

  1. Assistant Professor, Department of Computer Science & Engineering, Greater Kolkata College of Engineering and Management, West Bengal, India
  2. Assistant Professor, Department of Computer Science & Engineering, Greater Kolkata College of Engineering and Management, West Bengal, India
  3. Assistant Professor, Department of Computer Science & Engineering, Greater Kolkata College of Engineering and Management, West Bengal, India
  4. Associate Professor, Department of Information Technology, Narula Institute of Technology, West Bengal, India
  5. Associate Professor, Department of Information Technology, JIS College of Engineering, West Bengal, India
  6. Assistant Professor, Department of Information Technology, Narula Institute of Technology, West Bengal, India
  7. Assistant Professor, Department of Electrical Engineering, Greater Kolkata College of Engineering and Management, West Bengal, India
  8. Assistant Professor, Department of Electrical Engineering, Greater Kolkata College of Engineering and Management, West Bengal, India

Abstract

The accurate forecasting of polymer viscosity at various physicochemical conditions has been quite critical due to the nonlinear interactions and interrelations between the variables. This paper suggests a better hybrid modelling framework, which involves the use of Artificial Neural Networks (ANN) and more advanced versions of Quantum Particle Swarm Optimization (QPSO) to better predict polymer viscosity. The input parameters taken are, namely, log (shear rate), polymer concentration, NaCl concentration, Ca 2+ concentration, and temperature, and the output response is log(viscosity). The proposed framework systematically compares classical Particle Swarm Optimization (PSO), QPSO, Differential QPSO (DQPSO), and Gaussian QPSO (GQPSO) to optimize ANN weights and bias parameters. Of these, GQPSO uses a mechanism of Gaussian mutation to enhance population diversity, preventing premature convergence. Mean squared error (MSE), root mean square error (RMSE), relative error, and fitness value are used to assess the performance of each model on testing and cross-validation data. The findings show that predictive accuracy is definitely improved with the change of the optimization strategy towards GQPSO. The ANN-GQPSO model has the lowest values of RMSE and the greatest values of fitness, indicating better convergence and strength. Further analysis of the comparative study reveals that the Gaussian mutation improves the global search ability, which allows the model to break out of local minima and more accurately represent intricate nonlinear correlations in the data set. Besides the performance indicators, the study provides evidence of the significance of optimization schemes in modeling polymer systems with the help of neural networks. The model proposed, ANN-GQPSO, is a fixed and computationally valuable system to forecast the viscosity of polymers that does not have to make use of high-scale tests.

 

Keywords: Quantum Particle Swarm Optimization (QPSO), Gaussian Mutation, Artificial Neural Network (ANN), Polymer Rheology, Hybrid Optimization Framework

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How to cite this article:
Kakali Das, Amrita Mitra, Sagardeep Ghosh, Suchismita Maiti, Soumyabrata Saha, Pushpita Roy, Anubrata Mondal, Pijush Dutta. Study of an Improved Quantum Particle Swarm Optimization-Based Framework for Neural Network Optimization in Modelling of Polymer Data. Journal of Polymer & Composites. 2026; 14(04):-.
How to cite this URL:
Kakali Das, Amrita Mitra, Sagardeep Ghosh, Suchismita Maiti, Soumyabrata Saha, Pushpita Roy, Anubrata Mondal, Pijush Dutta. Study of an Improved Quantum Particle Swarm Optimization-Based Framework for Neural Network Optimization in Modelling of Polymer Data. Journal of Polymer & Composites. 2026; 14(04):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=248867


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Ahead of Print Subscription Original Research
Volume 14
04
Received 17/04/2026
Accepted 10/06/2026
Published 03/07/2026
Publication Time 77 Days
26

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