Advances in Metaheuristics and Learnheuristics (2019–2026): A Comprehensive Review, Taxonomy, and Future Directions
Year: 2026
Type: article
Abstract: This review examines more than 150 recent metaheuristics introduced between 2019 and 2026, emphasizing their significant impact and performance in solving complex optimization problems. In the past three decades, more than 500 metaheuristic algorithms have been proposed, with no sign of deceleration, resulting in a vast and growing landscape that makes it increasingly difficult to select, compare, and evaluate the most suitable methods for specific challenges. Our analysis considers several important aspects, including citation trends, application areas, the availability of source code, parameter configuration, the originality of proposed mechanisms and operators, and the ability of these methods to address common challenges such as stagnation and premature convergence. We also examine the practical applications and performance of the 15 most influential metaheuristic algorithms proposed over the past six years. Particular attention is given to learnheuristics, an emerging research area that combines machine learning with metaheuristic optimization to address dynamic and data-driven problems. To provide a clearer view of this growing field, we introduce a unified taxonomy covering both metaheuristics and learnheuristics. The taxonomy classifies these methods according to the level of learning integration, problem type, learning approach, and intended purpose. We believe that this classification can help researchers better understand the current state of the field and identify potential opportunities for future research. Received: 2 November 2025 | Revised: 28 July 2026 | Accepted: 28 August 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article, as no new data were created or analyzed in this study. Author Contribution Statement Tansel Dokeroglu: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing, Supervision, Project administration. Deniz Canturk: Conceptualization, Methodology, Software, Validation, Investigation, Resources, Data curation, Writing – original draft, Visualization, Supervision, Project administration.
Authors: Tansel Dokeroglu, Deniz Canturk
Institution: TED University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7225822579
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