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In recent years, gender detection has been an important problem in speech analysis, with many different applications. Different features of sound data such as pitch, median, and frequency are used for gender detection. In this study, a feature selection method based on metaheuristic optimization algorithms is proposed for gender detection from voice data. In the proposed method, the feature set representing the voice data in the most appropriate way is selected with optimization algorithms, and gender detection has been implemented with artificial intelligence algorithms using the obtained features. Nature-inspired metaheuristic optimization algorithms, which are capable of solving complex problems, are used to select features from voice data. Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Salp Swarm Algorithm (SSA), and Whale Optimization Algorithm (WOA) have been modeled for the first time for feature selection from voice data. A publicly accessible data set has been used to measure the efficiency of metaheuristic optimization algorithms. The performances of PSO, ACO, SSA, and WOA for feature selection have been compared in terms of three different criteria: fitness function value, accuracy value, and the number of selected features. After feature selection with metaheuristic optimization algorithms, Naive Bayes and Decision Tree algorithms have been applied to the new data sets obtained and the original data set. As a result of the analysis, it was observed that this method which uses metaheuristic optimization algorithms for feature selection increased the success rate in the results obtained with Naive Bayes and Decision Tree algorithms. [ABSTRACT FROM AUTHOR]

Real-world problems in engineering are often nonlinear or constrained design problems. For many reasons, an engineer wants to get the best design, not just any that works properly. The process of determining the best design is called optimization. With optimization, the best design of the problem is determined to achieve a specific objective function by providing the current constraints. In this study, three real-world engineering design problems are tried to be optimized, namely tension/compression spring, welded beam, and pressure vessel designs with various equalities and inequality constraints. In the optimization process, eight different algorithms are used, the best designs are created, and the optimum variables of the problems are determined. Optimization algorithms are selected from evolutionary-based, swarm-based, mathematics-based, and physics-based algorithms, which are sub-branches of metaheuristic algorithms. In addition, the results of the algorithms are compared with each other with the help of convergence curves and box graphs. The grey wolf algorithm is the algorithm that showed the most successful performance in all three problems. Besides, swarm-based, physics-based, and math-based algorithms performed better than other algorithms in optimizing real engineering problems. [ABSTRACT FROM AUTHOR]