Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/1674 2018 2018 Department of Mathematics, University of Mosul, Mosul, Iraq Firas A. Yonis AL AL-Taie Department of Statistics and Informatics, University of Mosul, Mosul, Iraq Zakariya Yahya Algamal Department of Mathematics, University of Mosul, Mosul, Iraq Omar Saber Qasim This paper investigates the process of selecting a hyperparameter for use in a kernel semiparametric regression model for fusion data, which is an important tool in various scientific study fields. The selection of the best model to use in advance is not a simple task, and one of the most fascinating current advances in the application is the use of hybrid metaheuristics algorithms to increase the exploration and exploitation capacity of traditional meta-heuristic algorithms. In this study, a hybrid optimization method that combines the pelican algorithm with the black hole algorithm is presented, which achieves a lower mean squared error (MSE) in comparison to other competing techniques. Data merging through the suggested hybrid metaheuristics algorithm gives superior performance in terms of computing time when compared to both the CV-method and the GCV-method. This work has practical implications for researchers and practitioners who use statistical modeling techniques in their work, especially those dealing with data merging for improved accuracy and efficiency. 2023 2023 57 69 10.54216/FPA.110104 https://www.americaspg.com/articleinfo/3/show/1674