Hybrid learning-driven golden jackal optimizer for reliable parameter estimation of nonlinear memristive chaotic systems

Accurate identification of parameters in chaotic and nonlinear systems is essential for ensuring precise modeling, control, and prediction of complex dynamical behaviors. However, conventional metaheuristic algorithms often struggle to maintain an effective balance between exploration and exploitation, leading to premature convergence and estimation inaccuracies. To address these challenges, this study proposes an enhanced golden jackal optimizer (en-GJO) that integrates three complementary mechanisms (Laplacian crossover learning, elite group learning, and opposition repair learning). These hybrid strategies collectively strengthen population diversity, accelerate convergence, and prevent stagnation, thereby improving both the global search capability and local refinement accuracy of the original GJO. The effectiveness of the en-GJO is first validated through extensive benchmarking on twenty-three standard test functions, including unimodal, multimodal, and fixed-dimensional multimodal problems. Comparative results against nine well-established metaheuristics (such as SSA, SCA, HHO, AEO, EO, GBO, RUN, and ARO) demonstrate that en-GJO achieves superior convergence precision and robustness, consistently yielding the lowest mean and standard-deviation values across all categories. To further verify its real-world applicability, the en-GJO is applied to the parameter identification of a memristive chaotic system, formulated as a nonlinear optimization problem using a least-squares-based objective function. Simulation results reveal that the proposed method attains the most accurate estimates of the system parameters \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left( {a,b,c,d} \right)$$\end{document}, with negligible deviation from their true values. Statistical analyses and convergence profiles confirm that en-GJO not only converges faster but also delivers more stable and repeatable performance than competing algorithms. In comparative evaluations with reported techniques such as PSO, ABC, SPSSA, GWO, POA, and FPPOA, the en-GJO achieves the smallest cost value (1.3850 x 10-13) and with a mean fitness of 1.0507 x 10-9 and a standard deviation of 2.5392 x 10-9, outperforming all compared algorithms by several orders of magnitude. The estimated system parameters converge to their true values with error rates below 0.001%, confirming the high accuracy, stability, and repeatability of the proposed approach. In summary, the proposed en-GJO offers a highly accurate, stable, and computationally efficient solution for parameter estimation in nonlinear and chaotic systems.