Using Opposition Golden Jackal Optimization Algorithm (OGJO) in Improving Some Kernel Semiparametric Models: A Comparative Study

Recent research and studies show widespread interest in semiparametric regression model analysis, which combines parametric and nonparametric components. This interest is because it gives accurate and effective statistical model estimates. This paper proposes to improve estimates of semiparametric regression models using opposition-based learning technology on the golden Jackal Optimization algorithm to increase the accuracy of these models, accelerate convergence, and expand the exploration area. The effectiveness of using this algorithm was evaluated by comparing it with the original algorithm before optimization and the most commonly used methods for estimating the model statistically, such as CV and GCV. Using simulation, the results showed that the improvement in the OBL-GJO algorithm in terms of accuracy and convergence speed outperformed the original algorithm and traditional methods by a large margin in calculating the simulation results of the kernel semiparametric regression models. We strongly advocate for applying the GJO algorithm across various domains within machine learning, particularly in the realms of deep learning and reinforcement learning. Furthermore, we have employed enhanced and evolved algorithms to optimize semiparametric regression models effectively. To address the challenges encountered by any algorithm operating within a vast search landscape, we suggest an in-depth exploration of optimization techniques and integrating diverse algorithms, which could lead to more robust and efficient solutions.