Feature Selection with Metaheuristic Algorithms: A Review of Recent Developments (2020–2025)

Authors

  • Saikat Gochhait Federal State Budgetary Educational Institution of Higher Education, Samara State Medical University, Ministry of Healthcare, Samara, Russia.

https://doi.org/10.48313/maa.v2i1.34

Abstract

Feature selection is a critical preprocessing step in machine learning, aimed at identifying relevant features from high-dimensional datasets to improve model performance and reduce computational cost. Due to its NP-hard nature, metaheuristic algorithms have gained prominence for efficiently navigating the vast search space. This review examines approximately 150 metaheuristic algorithms developed or refined between 2020 and 2025, categorized into Evolutionary, Physics-Based, Human-Social, and Swarm Intelligence approaches. Swarm Intelligence algorithms dominate recent advances, comprising 55% of the surveyed methods, reflecting their scalability and effectiveness in complex domains such as healthcare and cybersecurity. The review highlights algorithmic trends including hybridization, chaos-based diversity enhancement, and multi-objective optimization, and proposes future directions focused on adaptive, interpretable, and AI-integrated frameworks.

Keywords:

Metaheuristic, Optimization, Feature selection, Nondeterministic polynomial-hard, Machine learning

References

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Published

2025-11-22

Issue

Vol. 2 No. 1 (2025): Metaheuristic Algorithms with Applications (MAA)

Section

Articles

Categories

How to Cite

Gochhait, S. . (2025). Feature Selection with Metaheuristic Algorithms: A Review of Recent Developments (2020–2025). Metaheuristic Algorithms With Applications, 2(1). https://doi.org/10.48313/maa.v2i1.34

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