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Galoitica: Journal of Mathematical Structures and Applications

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Galoitica: Journal of Mathematical Structures and Applications
Full Length Article

Volume 13Issue 1PP: 57-75 • 2026

Improved Solution Methods for Initial Value Problems of Ordinary Differential Equations Using Advanced Optimization Techniques

Qasim Tayyeh 1*
1Department of Mechanical Techniques,Al-Nasiriya Technical Institute, Southern Technical University,Thi-Qar, Al-Nasiriya 64001, Iraq
* Corresponding Author.
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© 2026 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Received: October 28, 2025 Revised: December 21, 2025 Accepted: January 29, 2026

Abstract

Initial value problems (IVPs) of ordinary differential equations (ODEs) are ubiquitous in science and engineering applications, and the classical fourth-order Runge–Kutta (RK4) method is by far the most popular solver due to its good accuracy-to-cost ratio. Among all four-stage fourth-order explicit RK methods there are two free node parameters left after satisfying the eight B-series order conditions, thus allowing further systematic enhancements. Here we employ a hybrid multi-seed Particle Swarm Optimization (PSO)-Nelder–Mead algorithm to search for optimal RK node parameters (c₂, c₃) with respect to a minimax normalized objective over eight commonly used nonlinear benchmark problems. The resulting PSO-RK4 method with (c₂ = 0.323665, c₃ = 0.653527) retains both the exact same order of convergence and absolute stability region as the classical RK method called the 3/8-rule, but exhibits reduced maximum global error on each of the eight benchmarks when N = 300; average improvement of 27.9% with gains up to 46.0% on the Bernoulli equation, 29.7% on logistic growth, and 21.3% on exponential. Robustness of these gains with respect to multi-step-size (N = 100, 200, 300, 500) is demonstrated.

Keywords

Initial value problems Runge–Kutta methods Particle Swarm Optimization Nelder–Mead simplex hybrid metaheuristic optimization Butcher tableau Absolute stability Nonlinear ODEs

References

[1] J. Fang, W. Liu, L. Chen, S. Lauria, A. Miron, and X. Liu, "A survey of algorithms, applications and trends for particle swarm optimization," International Journal of Network Dynamics and Intelligence, vol. 2, no. 1, pp. 24–50, 2023, doi: 10.53941/ijndi0201002.

[2] T. M. Shami, A. A. El-Saleh, M. Alswaitti, Q. Al-Tashi, M. A. Summakieh, and S. Mirjalili, "Particle swarm optimization: A comprehensive survey," IEEE Access, vol. 10, pp. 10031–10061, 2022, doi: 10.1109/ACCESS.2022.3142859.

[3] I. Ahmadianfar, A. A. Heidari, A. H. Gandomi, X. Chu, and H. Chen, "RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method," Expert Systems with Applications, vol. 181, art. no. 115079, 2021, doi: 10.1016/j.eswa.2021.115079.

[4] R. A. Nascimento, Á. Barroca Neto, Y. S. de Freitas Bezerra, H. A. D. do Nascimento, L. dos Santos Lucena, and J. E. de Freitas, "A new hybrid optimization approach using PSO, Nelder-Mead Simplex and Kmeans clustering algorithms for 1D full waveform inversion," PLoS ONE, vol. 17, no. 12, art. no. e0277900, 2022, doi: 10.1371/journal.pone.0277900.

[5] T. Ouyang, H. Yu, and L. Jin, "Hybrid Harris hawks and Nelder-Mead optimization algorithm for engineering design problems," Expert Systems with Applications, vol. 175, art. no. 114765, 2021, doi: 10.1016/j.eswa.2021.114765.

[6] N. Majhi, R. Mishra, and G. Singh, "A novel hybrid genetic algorithm and Nelder-Mead approach and its application for parameter estimation," F1000Research, vol. 13, art. no. 1112, 2025, doi: 10.12688/f1000research.154598.3.

[7] M. Sutti and B. Vandereycken, "Implicit low-rank Riemannian schemes for the time integration of stiff partial differential equations," Journal of Scientific Computing, vol. 101, no. 1, art. no. 7, 2024, doi: 10.1007/s10915-024-02629-8.

[8] H. Ranocha, L. Dalcin, M. Parsani, and D. I. Ketcheson, "Optimized Runge-Kutta methods with automatic step size control for compressible computational fluid dynamics," Communications on Applied Mathematics and Computation, vol. 4, no. 4, pp. 1191–1228, 2022, doi: 10.1007/s42967-021-00159-w.

[9] H. Baty, "Solving stiff ordinary differential equations using physics informed neural networks (PINNs): Simple recipes to improve training of vanilla-PINNs," arXiv preprint, arXiv:2304.08289, 2023, doi: 10.48550/arXiv.2304.08289.

[10] M. Raissi, P. Perdikaris, and G. E. Karniadakis, "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations," Journal of Computational Physics, vol. 378, pp. 686–707, 2019, doi: 10.1016/j.jcp.2018.10.045.

[11] S. Conde, I. Fekete, and J. N. Shadid, "Embedded error estimation and adaptive step-size control for optimal explicit strong stability preserving Runge–Kutta methods," arXiv preprint, arXiv:1806.08693, 2022, doi: 10.48550/arXiv.1806.08693.

[12] H. Ranocha and J. Giesselmann, "Stability of step size control based on a posteriori error estimates," SMAI Journal of Computational Mathematics, vol. 10, pp. 137–164, 2024, doi: 10.5802/smai-jcm.108.

[13] Z. A. Anastassi, "Evolutionary optimisation of Runge–Kutta methods for oscillatory problems," Mathematics, vol. 13, no. 17, art. no. 2796, 2025, doi: 10.3390/math13172796.

[14] G. L. Goodship, L. Miralles-Pechuán, and S. O'Sullivan, "Optimizing 4th-order Runge–Kutta methods: A dynamic heuristic approach for efficiency and low storage," arXiv preprint, arXiv:2506.21465, 2025, doi: 10.48550/arXiv.2506.21465.

[15] T. E. Simos and C. Tsitouras, "On high-order Runge–Kutta pairs for linear inhomogeneous problems," Axioms, vol. 14, no. 4, art. no. 245, 2025, doi: 10.3390/axioms14040245.

[16] U. Habibah, F. F. Medrano, A. C. Permana, D. Ardiana, and Trisilowati, "An improved fifth-order Runge–Kutta method with higher accuracy and efficiency for solving initial value problems," Science and Technology Indonesia, vol. 10, no. 3, pp. 802–816, 2025, doi: 10.26554/sti.2025.10.3.802-816.

[17] H. A. El-Sattar, S. Kamel, M. H. Hassan, and F. Jurado, "Optimal sizing of an off-grid hybrid photovoltaic/biomass gasifier/battery system using a quantum model of Runge Kutta algorithm," Energy Conversion and Management, vol. 258, art. no. 115539, 2022, doi: 10.1016/j.enconman.2022.115539.

[18] J. Lian, G. Hui, L. Ma, T. Zhu, X. Wu, A. A. Heidari, Y. Chen, and H. Chen, "Parrot optimizer: Algorithm and applications to medical problems," Computers in Biology and Medicine, vol. 172, art. no. 108064, 2024, doi: 10.1016/j.compbiomed.2024.108064.

[19] H. Su, D. Zhao, A. A. Heidari, L. Liu, X. Zhang, M. Mafarja, and H. Chen, "RIME: A physics-based optimization," Neurocomputing, vol. 532, pp. 183–214, 2023, doi: 10.1016/j.neucom.2023.02.010.

[20] M. Calvo, J. I. Montijano, and L. Rández, "Modified singly-Runge-Kutta-TASE methods for the numerical solution of stiff differential equations," arXiv preprint, arXiv:2407.01785, 2024, doi: 10.48550/arXiv.2407.01785.

[21] H. Ranocha and D. I. Ketcheson, "Energy stability of explicit Runge-Kutta methods for non-autonomous or nonlinear problems," SIAM Journal on Numerical Analysis, vol. 58, no. 6, pp. 3382–3405, 2020, doi: 10.1137/19M1290346.

[22] L. Einkemmer, "An adaptive step size controller for iterative implicit methods," Applied Numerical Mathematics, vol. 132, pp. 182–204, 2018, doi: 10.1016/j.apnum.2018.06.002.

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Tayyeh, Qasim . "Improved Solution Methods for Initial Value Problems of Ordinary Differential Equations Using Advanced Optimization Techniques." Galoitica: Journal of Mathematical Structures and Applications, vol. Volume 13, no. Issue 1, 2026, pp. 57-75. DOI: https://doi.org/10.54216/GJMSA.130105
Tayyeh, Q. (2026). Improved Solution Methods for Initial Value Problems of Ordinary Differential Equations Using Advanced Optimization Techniques. Galoitica: Journal of Mathematical Structures and Applications, Volume 13(Issue 1), 57-75. DOI: https://doi.org/10.54216/GJMSA.130105
Tayyeh, Qasim . "Improved Solution Methods for Initial Value Problems of Ordinary Differential Equations Using Advanced Optimization Techniques." Galoitica: Journal of Mathematical Structures and Applications Volume 13, no. Issue 1 (2026): 57-75. DOI: https://doi.org/10.54216/GJMSA.130105
Tayyeh, Q. (2026) 'Improved Solution Methods for Initial Value Problems of Ordinary Differential Equations Using Advanced Optimization Techniques', Galoitica: Journal of Mathematical Structures and Applications, Volume 13(Issue 1), pp. 57-75. DOI: https://doi.org/10.54216/GJMSA.130105
Tayyeh Q. Improved Solution Methods for Initial Value Problems of Ordinary Differential Equations Using Advanced Optimization Techniques. Galoitica: Journal of Mathematical Structures and Applications. 2026;Volume 13(Issue 1):57-75. DOI: https://doi.org/10.54216/GJMSA.130105
Q. Tayyeh, "Improved Solution Methods for Initial Value Problems of Ordinary Differential Equations Using Advanced Optimization Techniques," Galoitica: Journal of Mathematical Structures and Applications, vol. Volume 13, no. Issue 1, pp. 57-75, 2026. DOI: https://doi.org/10.54216/GJMSA.130105
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