A Systematic Review of Metaheuristic-Metaheuristic (MH-MH) Hybridizations for Optimization

Abstract

This systematic review following the PRISMA framework analyzes metaheuristic-metaheuristic (MH-MH) hybridizations published between January and October 2024 to uncover patterns of algorithmic dominance, functional roles and integration strategies, metaphor-based partnerships, domain mappings and evaluation orientations. Frequency mapping of 105 canonical algorithms identified Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA) as the three most recurrent MHs, appearing in 20 (19.2%), 14 (13.5%), and 9 (8.7%) studies, respectively. Functional and structural analysis focused on PSO as the leading applied MH revealed its dual role as a global exploration driver and local exploitation engine, supported by balanced adoption of cooperative (45%) and sequential (45%) integration strategies. In comparison, embedded configurations accounted for the remaining 10%. Metaphor-based partner classification of the three most frequently applied canonical MHs showed that most hybrids combined flying and terrestrial swarm algorithms. Evaluation orientation analysis of the three most applied MHs indicated a gradual shift from benchmark-based validation toward domain-driven assessment, particularly in energy systems (30%), biomedical and health analytics (20%), and networking applications (15%). The review demonstrates that MH-MH hybrid success in 2024 is shaped by three interdependent design principles: Algorithmic complementarity that ensures exploration-exploitation balance, metaphorical congruence that sustains behavioral coherence, and evaluation coherence that aligns methodological rigor with domain relevance. These findings establish a unified empirical and theoretical foundation for the development of interpretable, adaptive, and reproducible hybrids.