High-precision parameter identification for the lorenz system via starfish optimization algorithm

Parameter estimation plays a vital role in the accurate modeling and control of chaotic systems due to their inherent sensitivity to initial conditions and system parameters. This study introduces a novel approach for parameter identification in the Lorenz system using the starfish optimization algorithm (SFOA). The problem is formulated as a nonlinear optimization task, and the SFOA is employed to minimize the discrepancy between the observed and simulated trajectories. The proposed method is evaluated across multiple independent runs and benchmarked against several well-known optimization techniques. The results demonstrate that the SFOA achieves highly accurate and consistent parameter estimates, exhibiting superior robustness and convergence behavior compared to other methods. These findings suggest that SFOA is a promising candidate for addressing parameter estimation challenges in chaotic and nonlinear dynamical systems.