Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)

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A Metaheuristic Evolutionary Algorithm for Accurate Global Gold Rate Prediction with ANFIS- Particle Swarm Optimization Model

Authors
N. Hemalatha1, *, A. Inba Rexy2, R. R. Aswiga3
1Bharath Institute of Science and Technology, Bharath Institute of Higher Education and Research, Chennai, India
2LOYOLA – ICAM College of Engineering and Technology (LICET), Chennai, India
3SRM Institute of Science and Technology, Katankulathur, Chennai, India
*Corresponding author. Email: rnhemaa@gmail.com
Corresponding Author
N. Hemalatha
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-654-8_61How to use a DOI?
Keywords
Neural Network; Particle Swarm Optimization; ANFIS; Root Mean Square
Abstract

The global market value of gold price decides the economic and the monetary systems of a country. In the volatile gold market, a forecasting prediction model is needed to lower the risk of financial deprivation in the event of a sudden market crisis. The proposed work is to build prediction models for monitoring the daily gold price variations using the machine learning algorithm based models. Adaptive NeuroFuzzy Inference System (ANFIS)-Particle Swarm Optimization (PSO) and the Neural Network (NN) algorithms. ANFIS-PSO and Neural Network (NN) based machine learning models have an outsized number of features and the parameters like Root Mean Square Error (RMSE) and accuracy are utilized in estimating the market value of gold rate. The performance analysis is executed with the ANFIS-PSO model in the monitoring of gold rates, by comparing it with the NN model. The results suggest that ANFIS-PSO model incorporating both neural network and fuzzy logic with the PSO technique is a powerful machine learning tool in forecasting the gold rates.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
Series
Advances in Engineering Research
Publication Date
24 April 2026
ISBN
978-94-6239-654-8
ISSN
2352-5401
DOI
10.2991/978-94-6239-654-8_61How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - N. Hemalatha
AU  - A. Inba Rexy
AU  - R. R. Aswiga
PY  - 2026
DA  - 2026/04/24
TI  - A Metaheuristic Evolutionary Algorithm for Accurate Global Gold Rate Prediction with ANFIS- Particle Swarm Optimization Model
BT  - Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
PB  - Atlantis Press
SP  - 783
EP  - 792
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-654-8_61
DO  - 10.2991/978-94-6239-654-8_61
ID  - Hemalatha2026
ER  -