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Fusion: Practice and Applications
Volume 11 , Issue 1, PP: 57-69 , 2023 | Cite this article as | XML | Html |PDF

Title

A Hybrid Pelican Optimization Algorithm and Black Hole Algorithm for Kernel Semi-Parametric Fusion Modeling

  Firas A. Yonis AL-Taie 1 * ,   Zakariya Yahya Algamal 2 ,   Omar Saber Qasim 3

1  Department of Mathematics, University of Mosul, Mosul, Iraq
    (firas_a.al_taie@uomosul.edu.iq)

2  Department of Statistics and Informatics, University of Mosul, Mosul, Iraq
    (zakariya.algamal@uomosul.edu.iq)

3  Department of Mathematics, University of Mosul, Mosul, Iraq
    (omar.saber@uomosul.edu.iq)


Doi   :   https://doi.org/10.54216/FPA.110104

Received: December 04, 2022 Accepted: March 09, 2023

Abstract :

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.

Keywords :

Black hole algorithm (BHA); Pelican optimization algorithm (POA); semiparametric model; kernel methods; cross-validation; data fusion. 

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Cite this Article as :
Style #
MLA Firas A. Yonis AL-Taie, Zakariya Yahya Algamal, Omar Saber Qasim. "A Hybrid Pelican Optimization Algorithm and Black Hole Algorithm for Kernel Semi-Parametric Fusion Modeling." Fusion: Practice and Applications, Vol. 11, No. 1, 2023 ,PP. 57-69 (Doi   :  https://doi.org/10.54216/FPA.110104)
APA Firas A. Yonis AL-Taie, Zakariya Yahya Algamal, Omar Saber Qasim. (2023). A Hybrid Pelican Optimization Algorithm and Black Hole Algorithm for Kernel Semi-Parametric Fusion Modeling. Journal of Fusion: Practice and Applications, 11 ( 1 ), 57-69 (Doi   :  https://doi.org/10.54216/FPA.110104)
Chicago Firas A. Yonis AL-Taie, Zakariya Yahya Algamal, Omar Saber Qasim. "A Hybrid Pelican Optimization Algorithm and Black Hole Algorithm for Kernel Semi-Parametric Fusion Modeling." Journal of Fusion: Practice and Applications, 11 no. 1 (2023): 57-69 (Doi   :  https://doi.org/10.54216/FPA.110104)
Harvard Firas A. Yonis AL-Taie, Zakariya Yahya Algamal, Omar Saber Qasim. (2023). A Hybrid Pelican Optimization Algorithm and Black Hole Algorithm for Kernel Semi-Parametric Fusion Modeling. Journal of Fusion: Practice and Applications, 11 ( 1 ), 57-69 (Doi   :  https://doi.org/10.54216/FPA.110104)
Vancouver Firas A. Yonis AL-Taie, Zakariya Yahya Algamal, Omar Saber Qasim. A Hybrid Pelican Optimization Algorithm and Black Hole Algorithm for Kernel Semi-Parametric Fusion Modeling. Journal of Fusion: Practice and Applications, (2023); 11 ( 1 ): 57-69 (Doi   :  https://doi.org/10.54216/FPA.110104)
IEEE Firas A. Yonis AL-Taie, Zakariya Yahya Algamal, Omar Saber Qasim, A Hybrid Pelican Optimization Algorithm and Black Hole Algorithm for Kernel Semi-Parametric Fusion Modeling, Journal of Fusion: Practice and Applications, Vol. 11 , No. 1 , (2023) : 57-69 (Doi   :  https://doi.org/10.54216/FPA.110104)