Optimized number of bats of binary bat algorithm for feature selection

Due to its outstanding performance despite its sensitivity to input parameters, the bat algorithm (BA) is a popularly used metaheuristic algorithm in numerous applications. The algorithm's convergence behavior is affected by the algorithm's frequency, the number of bats, pulse emission rate, and loudness parameters. These parameters must be set to their optimal values to increase the outcome's quality. This research aims to find the optimal number of bats of the binary bat algorithm (BBA). In this situation, the fitness criterion for the fitness function will be a classifier error rate. K-nearest neighbors (KNN) are used as the classifier for error rate calculation and validation methods. This study is conducted using 13 datasets with various dimensional sizes. The analysis results indicate that the number of bats substantially impacts the classification accuracy. Based on the results, the optimal number of bats must be considered when tuning the algorithm's parameters to achieve adequate performance.