Volume 13 • Issue 1 • PP: 57-75 • 2026
Improved Solution Methods for Initial Value Problems of Ordinary Differential Equations Using Advanced Optimization Techniques
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
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.
Cite This Article
Choose your preferred format