Solving Differential Equation Systems Using Metaheuristics

Liviu Octavian Mafteiu-Scai, Alexander Hauer, Roxana Teodora Mafteiu-Scai

© 2026 Liviu Octavian Mafteiu-Scai, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International. (CC BY-NC 4.0).

Citation Information: SAR Journal. Volume 9, Issue 2, Pages 120-131, ISSN 2619-9955, https://doi.org/10.18421/SAR92-04, June 2026.

Received: 15 May 2026.
Revised: 17 June 2026.
Accepted: 23 June 2026.
Published: 27 June 2026.

Abstract:

Solving systems of ordinary differential equations is important because they model interactions among variables, enabling control and prediction in many real-world problems. This paper analyzes the performance of five metaheuristic algorithms in solving these systems and compares them with two classical mathematical methods.

Keywords – particle swarm optimization, genetic algorithms, ant colony optimization, differential evolution, Runge–Kutta, systems of differential equations.

                   

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