Dragonfly Algorithm and Variants for Feature Selection: A Review
P T Prinson, Angelina Geetha · 2023
Ultimately, the key to successfully analysing real-world problems is to be diligent and thorough. It is important to carefully consider all available data, remove any redundant or unnecessary information, and approach the analysis with an open mind. By doing so, it is possible to gain valuable insights and make informed decisions that can help solve complex real-world problems. By removing redundant, irrelevant, and noisy data, feature selection (FS), a difficult machine learning problem, attempts to lessen the number of features while maintaining a decent degree of classification accuracy. Feature selection is intended to reduce features. This immediately reduces the search space and aids machine learning algorithms in using only the most significant features. A review of Dragonfly algorithms and variants is presented in this paper, emphasizing their main characteristics. Eventually, the conclusion concentrates on the present research on Dragonfly Algorithm, emphasizing its limitations with suggested solutions that lead to future directions