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 realworld
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.