TY - GEN
T1 - FHOPDT-Based Fractional-Order System Identification via PSO and GWO
T2 - 21st International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2026
AU - Idir, Abdelhakim
AU - Gude, Juan J.
AU - Camacho, Oscar
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - This paper presents a comparative study of fractional-order system identification using Particle Swarm Optimization (PSO) and the Grey Wolf Optimizer (GWO). A challenging benchmark problem involving a high-order fractional system with repeated poles and significant memory effects is considered. Model parameters are estimated by minimizing the error between the true system response and that of the identified model. The performance of both optimization algorithms is evaluated through time- and frequency-domain analyses, as well as convergence behavior and statistical metrics, including mean squared error (MSE), root mean square error (RMSE), and integral absolute error (IAE). Results indicate that GWO consistently outperforms PSO across all evaluation criteria, achieving approximately an 80% reduction in MSE and over a 60% improvement in IAE. Furthermore, GWO demonstrates faster and more stable convergence, avoids premature stagnation, and accurately identifies all system parameters, including the correct system order (n=5), whereas PSO converges to an incorrect model structure. These findings highlight the robustness and effectiveness of GWO for the identification of complex fractional-order systems.
AB - This paper presents a comparative study of fractional-order system identification using Particle Swarm Optimization (PSO) and the Grey Wolf Optimizer (GWO). A challenging benchmark problem involving a high-order fractional system with repeated poles and significant memory effects is considered. Model parameters are estimated by minimizing the error between the true system response and that of the identified model. The performance of both optimization algorithms is evaluated through time- and frequency-domain analyses, as well as convergence behavior and statistical metrics, including mean squared error (MSE), root mean square error (RMSE), and integral absolute error (IAE). Results indicate that GWO consistently outperforms PSO across all evaluation criteria, achieving approximately an 80% reduction in MSE and over a 60% improvement in IAE. Furthermore, GWO demonstrates faster and more stable convergence, avoids premature stagnation, and accurately identifies all system parameters, including the correct system order (n=5), whereas PSO converges to an incorrect model structure. These findings highlight the robustness and effectiveness of GWO for the identification of complex fractional-order systems.
KW - Fractional-order systems
KW - Grey Wolf Optimizer (GWO)
KW - Particle Swarm Optimization (PSO)
KW - System identification
UR - https://www.scopus.com/pages/publications/105042895193
U2 - 10.1007/978-3-032-29254-4_53
DO - 10.1007/978-3-032-29254-4_53
M3 - Contribución a la conferencia
AN - SCOPUS:105042895193
SN - 9783032292537
T3 - Communications in Computer and Information Science
SP - 662
EP - 674
BT - Soft Computing Models in Industrial and Environmental Applications - 21st International Conference, SOCO 2026, Proceedings
A2 - Corchado, Emilio
A2 - Quintián, Héctor
A2 - Jove, Esteban
A2 - Troncoso Lora, Alicia
A2 - Martínez Álvarez, Francisco
A2 - García Bringas, Pablo
A2 - Fosci, Paolo
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 18 June 2026 through 19 June 2026
ER -