Summary Scientific and technological advancements lead to the continuous generation of a large amount of data. These datasets are analyzed computationally to reveal patterns and trends. While the presence of noisy and irrelevant features or attributes in these datasets is unavoidable, they negatively impact the performance of classification techniques. Feature selection is a method to pre‐process these datasets by selecting the most informative features while concurrently improving the classification accuracy. Recently, several metaheuristic algorithms were employed in this feature selection process, including particle swarm optimization (PSO). PSO is prominent in the field of feature selection due to its simplicity and global search abilities. However, it may get stuck in local optima. To solve this problem, a new update mechanism in PSO is proposed and the PSO is hybridized with a local search method. To evaluate the performance of the proposed algorithm, benchmark datasets from the University of California in Irvine (UCI) repository were utilized, the k‐nearest neighbor as the classifier. Results show that the proposed feature selection technique outperforms other optimization algorithms on these feature selection problems.

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Metaheuristic algorithms have proven to be quite effective at solving global optimization issues, particularly feature selection difficulties. This class of algorithms often uses a specialized local search technique as an inner component or as a post-processing mechanism to improve the performance of their search process. This paper presents a comprehensive survey of the use of local search methods integrated into metaheuristic algorithms for optimizing the feature selection process. Based on the manner of operation, the local search methods examined in this study were classed as one-way or two-way. In addition, practical suggestions were also discussed to point out possible future directions.