Applications, classifications, and challenges: a comprehensive evaluation of recently developed metaheuristics for search and analysis
Muhammad Suhail Shaikh, Saurav Raj, Gengzhong Zheng, Senlin Xie, Chang Wang, Xiaoqing Dong, Yifan Lin, Chunwu Wang, Naveed Ur Rehman Junejo, Chunwu Wang, Naveed Ur Rehman Junejo
Abstract
Metaheuristic algorithms (MAs) are powerful tools for solving complex optimization problems across diverse domains. This comprehensive study analyzes 162 MAs through a unified multi-criteria taxonomy classifying algorithms by control parameters (parameter-free, low-parameter, high-parameter), inspiration sources (biological, physical, human-based), search space scope, and exploration–exploitation balance—alongside bibliometric assessment of publications from 2000 to 2024 (including articles, reviews, books, and conference papers). We evaluate the time complexity of 24 highly cited MAs and their real-world applications in engineering, healthcare, and energy systems. Results demonstrate that different MAs exhibit algorithmic simplicity , often requiring only a small number of control parameters, and are capable of efficient global search. Additionally, several algorithms demonstrate strong adaptability for hybridization with other techniques. However, certain methods are prone to premature convergence, primarily due to unbalanced exploration and exploitation dynamic s . The proposed work also critically examines metaphor-inspired algorithms, whose contributions are critically evaluated in light of their conceptual complexity. The rise of such methods has led to redundancy and fragmentation in the field, as many reframe familiar optimization principles using superficial metaphors rather than advancing core algorithmic mechanisms. Bibliometric analysis reveals accelerated growth in MA research (64% journal articles, 31% conference papers, 3% from reviews, 1% from conference reviews, and 1% from book chapters). Similarly, in terms of classification, human-inspired methods constitute the largest category (45%), followed by evaluation-inspired (33%), swarm-inspired (14%), and game-inspired and physics-based algorithms (4%). This work consolidates two decades of advancements, identifies critical performance limitations, and provides practical guidelines for algorithm selection and hybridization to enhance optimization efficiency.