Universiti Teknologi Malaysia Institutional Repository

Binary particle swarm optimization for variables selection optimization in Taguchi’s T-Method

Harudin, N. and Jamaludin, K. R. and Ramlie, F. and Muhtazaruddin, M. N. and Che Razali, Che Munira and Muhamad, W. Z. A. W. (2020) Binary particle swarm optimization for variables selection optimization in Taguchi’s T-Method. MATEMATIKA, 36 (1). pp. 69-84. ISSN 0127-9602


Official URL: https://dx.doi.org/10.11113/matematika.v36.n1.1181


Prediction analysis has drawn significant interest in numerous fields Taguchi’s T-Method is a prediction tool that was practically developed to predict even with a limited sample data. It was developed explicitly for multivariate system prediction by relying on historical data as the baseline model and adapting the signal to noise ratio (SNR) as well as zero proportional concepts in strengthening its robustness. Orthogonal array (OA) in T-Method is a variable selection optimization technique in improving the prediction accuracy as well as helping to eliminate variables that may deteriorate the overall performance. However, the limitation of OA in dealing with higher multivariate data restrains the optimization accuracy. The binary particle swarm optimization used in this study helps to cater to the limitation of OA as well as optimizing the variable selection process for better prediction accuracy. A comparison between the T-Method+OA and T- Method+BPSO in four different case studies shows that the T-Method+BPSO performs better with a higher coefficient of determination (R2) value and means relative error (MRE) value compared to the T-Method+OA. The T-Method with the BPSO element as variables screening optimization is able to increase or even maintain the prediction accuracy for cases that are normally distributed, have a high R2 value, and with low sample data.

Item Type:Article
Uncontrolled Keywords:Binary Particle Swarm Optimization, Optimum variable selection, Small sample data, Prediction, Taguchi’s T-Method
Subjects:Q Science > QA Mathematics
ID Code:85556
Deposited By: Fazli Masari
Deposited On:30 Jun 2020 16:50
Last Modified:30 Jun 2020 16:50

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