Efficiency and Accuracy in Quadratic Curve Fitting: A Comparative Analysis of Optimization Techniques

A Comparative Analysis of Optimization Techniques

  • Ahmed Hasan Alridha Ministry of Education - Iraq

Abstract

In this paper, we investigate an optimization methods might be applied for solving curve fitting by making use of a quadratic model. To discover the ideal parameters for the quadratic model, synthetic experimental data is generated, and then two unique optimization approaches, namely differential evolution and the Nelder-Mead algorithm, are applied to the problem in order to find the optimal values for those parameters. The mean squared error as well as the correlation coefficient are both metrics that are incorporated into the objective function. When the results of these algorithms are compared, trade-offs between the rate of convergence and the quality of the fit are revealed. This work sheds light on the necessity of selecting proper optimization algorithms for specific circumstances and provides insights into the balance that must be struck between accurate curve fitting and efficient use of computational resources in the process of curve fitting.

Downloads

Download data is not yet available.

References

[1] T. S. Ahearn, R. T. Staff, T. W. Redpath, and S. I. K. Semple, "The use of the Levenberg–Marquardt curve-fitting algorithm in pharmacokinetic modelling of DCE-MRI data," Physics in Medicine & Biology, vol. 50, no. 9, pp. N85, 2005.
[2] H. P. Gavin, "The Levenberg-Marquardt algorithm for nonlinear least squares curve-fitting problems," Department of civil and environmental engineering, Duke University, 2019.
[3] P. Irwin, S. Tu, W. Damert, and J. Phillips, "A MODIFIED GAUSS‐NEWTON ALGORITHM AND NINETY‐SIX WELL MICRO‐TECHNIQUE FOR CALCULATING MPN USING EXCEL SPREADSHEETS 1," Journal of Rapid Methods & Automation in Microbiology, vol. 8, no. 3, pp. 171-191, 2000.
[4] S. G. Dastidar, "Gompertz: A scilab program for estimating gompertz curve using Gauss-Newton method of least squares," Journal of Statistical Software, vol. 15, pp. 1-12, 2006.
[5] J. M. Whitacre, "Survival of the flexible: explaining the recent popularity of nature-inspired optimization within a rapidly evolving world," Computing, vol. 93, no. 2-4, pp. 135-146, 2011.
[6] D. K. Sarmah, "A survey on the latest development of machine learning in genetic algorithm and particle swarm optimization," Optimization in Machine Learning and Applications, pp. 91-112, 2020.
[7] K. R. Opara and J. Arabas, "Differential Evolution: A survey of theoretical analyses," Swarm and evolutionary computation, vol. 44, pp. 546-558, 2019.
[8] A. Rajasekhar, N. Lynn, S. Das, and P. N. Suganthan, "Computing with the collective intelligence of honey bees–a survey," Swarm and Evolutionary Computation, vol. 32, pp. 25-48, 2017.
[9] D. Izci, B. Hekimoğlu, and S. Ekinci, "A new artificial ecosystem-based optimization integrated with Nelder-Mead method for PID controller design of buck converter," Alexandria Engineering Journal, vol. 61, no. 3, pp. 2030-2044, 2022.
[10] L. Kong, S. Mirjalili, V. Snášel, J. S. Pan, A. Raj, R. V. Kahankova, and M. Radek, "Analysis on population-based algorithm optimized filter for non-invasive fECG extraction," Applied Soft Computing, vol. 142, p. 110323, 2023.
[11] C. Li, G. Sun, L. Deng, L. Qiao, and G. Yang, "A population state evaluation-based improvement framework for differential evolution," Information Sciences, vol. 629, pp. 15-38, 2023.
[12] Y. Ozaki, M. Yano, and M. Onishi, "Effective hyperparameter optimization using Nelder-Mead method in deep learning," IPSJ Transactions on Computer Vision and Applications, vol. 9, pp. 1-12, 2017.
[13] A. W. Mohamed and A. K. Mohamed, "Adaptive guided differential evolution algorithm with novel mutation for numerical optimization," International Journal of Machine Learning and Cybernetics, vol. 10, pp. 253-277, 2019.
[14] M. Becker, M. Jouda, A. Kolchinskaya, and J. G. Korvink, "Deep regression with ensembles enables fast, first-order shimming in low-field NMR," Journal of Magnetic Resonance, vol. 336, p. 107151, 2022.
Published
2023-10-31
How to Cite
ALRIDHA, Ahmed Hasan. Efficiency and Accuracy in Quadratic Curve Fitting: A Comparative Analysis of Optimization Techniques. Indonesian Journal of Applied Mathematics, [S.l.], v. 3, n. 2, p. 8-14, oct. 2023. ISSN 2774-2016. Available at: <https://journal.itera.ac.id/index.php/indojam/article/view/1575>. Date accessed: 03 may 2024. doi: https://doi.org/10.35472/indojam.v3i2.1575.