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Dipper Throated Optimization Algorithm for Unconstrained Function and Feature Selection

Ali E. Takieldeen1, El-Sayed M. El-kenawy1,2, Mohammed Hadwan3,4,5,*, Rokaia M. Zaki6,7

1 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, 35712, Egypt
2 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt
3 Department of Information Technology, College of Computer, Qassim University, Buraydah, 51452, Saudi Arabia
4 Department of Computer Science, College of Applied Sciences, Taiz University, Taiz, Yemen
5 Intelligent Analytics Group (IAG), College of Computer, Qassim University, Buraydah, Saudi Arabia
6 Higher Institute of Engineering and Technology, Kafrelsheikh, Egypt
7 Department of Electrical Engineering, Shoubra Faculty of Engineering, Benha University, Egypt

* Corresponding Author: Mohammed Hadwan. Email: email

Computers, Materials & Continua 2022, 72(1), 1465-1481. https://doi.org/10.32604/cmc.2022.026026

Abstract

Dipper throated optimization (DTO) algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated bird. DTO has its unique hunting technique by performing rapid bowing movements. To show the efficiency of the proposed algorithm, DTO is tested and compared to the algorithms of Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) based on the seven unimodal benchmark functions. Then, ANOVA and Wilcoxon rank-sum tests are performed to confirm the effectiveness of the DTO compared to other optimization techniques. Additionally, to demonstrate the proposed algorithm's suitability for solving complex real-world issues, DTO is used to solve the feature selection problem. The strategy of using DTOs as feature selection is evaluated using commonly used data sets from the University of California at Irvine (UCI) repository. The findings indicate that the DTO outperforms all other algorithms in addressing feature selection issues, demonstrating the proposed algorithm's capabilities to solve complex real-world situations.

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APA Style
Takieldeen, A.E., El-kenawy, E.M., Hadwan, M., Zaki, R.M. (2022). Dipper throated optimization algorithm for unconstrained function and feature selection. Computers, Materials & Continua, 72(1), 1465-1481. https://doi.org/10.32604/cmc.2022.026026
Vancouver Style
Takieldeen AE, El-kenawy EM, Hadwan M, Zaki RM. Dipper throated optimization algorithm for unconstrained function and feature selection. Comput Mater Contin. 2022;72(1):1465-1481 https://doi.org/10.32604/cmc.2022.026026
IEEE Style
A.E. Takieldeen, E.M. El-kenawy, M. Hadwan, and R.M. Zaki, “Dipper Throated Optimization Algorithm for Unconstrained Function and Feature Selection,” Comput. Mater. Contin., vol. 72, no. 1, pp. 1465-1481, 2022. https://doi.org/10.32604/cmc.2022.026026

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cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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