Open Access

ARTICLE

Dipper Throated Optimization Algorithm for Unconstrained Function and Feature Selection

by Ali E. Takieldeen¹, El-Sayed M. El-kenawy^1,2, Mohammed Hadwan^3,4,5,*, Rokaia M. Zaki^6,7

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:

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

Received 13 December 2021; Accepted 13 January 2022; Issue published 24 February 2022

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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Citations