<p>Metaheuristic algorithms have become a widely adopted approach for addressing feature selection problems in high-dimensional datasets. Among these methods, Particle Swarm Optimization (PSO) has received attention due to its simple structure, efficient search capability, and adaptability to different optimization scenarios. As a result, numerous PSO-based feature selection methods have been proposed in recent years, each introducing various modifications to improve search performance and subset quality. Despite this rapid development, a structured analysis that highlights the strengths, limitations, and practical implications of these approaches remains necessary. This survey provides a systematic examination of prominent PSO-based feature selection algorithms reported in the literature. The reviewed methods are analyzed and compared with respect to several important aspects, including search behavior, strategies used to balance exploration and exploitation, design of fitness functions for evaluating feature subsets, and commonly used evaluation criteria such as classification accuracy, dimensionality reduction rate, and computational cost. The analysis highlights the main limitations of PSO-based feature selection, including a tendency to premature convergence, sensitivity to parameter settings, and scalability issues in high-dimensional environments. Based on these observations, several open research challenges are identified and potential directions for future work are outlined in order to improve the applicability of PSO-driven feature selection methods.</p>