This paper presents a concise literature review of metaheuristic algorithms in feature selection, spanning publications from 2019 to 2023. The study acknowledges the role of wrapper methods and metaheuristics, noting their ability to yield enhanced results. It highlights the prevalent use of Particle Swarm Optimization, Grey Wolf Optimizer, and Genetic Algorithm in this context. Additionally, the research explores trends and approaches in binarization within metaheuristics, distinguishing between straightforward binarization and more elaborate methods. A central theme of the study is the investigation of hybridization in metaheuristics, demonstrating the use and integration of multiple algorithms in search of improvement in performance. The paper also discusses strategies for refining metaheuristic performance, such as chaotic maps and local search. It examines the emerging domain of multi-objective metaheuristics, which is particularly relevant for addressing real-world problems with competing objectives. The study highlights the dynamic and innovative potential of metaheuristic-based feature selection methodologies.

Technological advances and the digitization of information have allowed us to obtain a large amount of data from different processes such as medicine, commerce, mining, among others. All this data has been used by different researchers in machine learning techniques to accelerate the decision making process of professionals. Machine learning techniques are very sensitive to data, so it is necessary to perform a cleaning to remove irrelevant and redundant information. This information removal is known as the feature selection problem. This paper presents the Pendulum Search Algorithm applied to solve the feature selection problem. Since the Pendulum Search Algorithm is a metaheuristic designed for continuous optimization problems, a binarization process is performed using the two-step technique. Preliminary results indicate that our proposal obtains competitive results compared to other metaheuristics extracted from the literature that solve well-known benchmarks.