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.
Highlights
Optimization is the process of obtaining the greatest or least objective function value for a set of inputs
The proposed Dipper Throated Optimization (DTO) algorithm is tested 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 [19]
analysis of variance (ANOVA) and Wilcoxon rank-sum tests are performed to confirm the effectiveness of the proposed algorithm compared to other optimization techniques
Summary
Optimization is the process of obtaining the greatest or least objective function value for a set of inputs It is the subject of various machine techniques that draw on artificial neural networks. The information about the objective function utilized and used throughout the optimization process depends on one technique of optimization classification [4]. Perhaps the significant difference between optimization techniques is identifying the destination function in one location [6] It means that the first derivative of the feature may be used to identify a possible solution (gradient or route). Almost every area of life is involved, much from engineering to business, holiday preparation to internet travel [8] The use of those readily available resources must be maximized due to the continuous scarcity of money, resources, and time. The solution with the most distinctive features and the maximum classification accuracy is deemed optimal [13]
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