Comprehensive Review and Hybrid Evolution of Teaching–Learning-Based Optimization
Аннотация
Teaching–Learning-Based Optimization (TLBO) stands out as a novel population‐based metaheuristic inspired by the pedagogical process in a classroom, in which a teacher imparts knowledge to learners and the learners enhance their performance by mutual interaction. This paper provides a comprehensive review of TLBO along with its extensive hybrid and intelligent extensions developed over recent years. We examine the fundamental algorithmic principles of TLBO—its parameter‐free nature, two-phase (teacher and learner) approach, and inherent simplicity—and contrast its performance across a range of benchmarks and real-world engineering optimization problems. In addition, we survey various taxonomic categories such as adaptive TLBO, multi-objective TLBO, discrete variants, and hybrids that integrate TLBO with other metaheuristics including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Robust Tabu Search (RTS), and Harmony Search (HS)6. Special attention is given to recent innovations in hybrid frameworks, such as the TLBO–RTS and TLBO–CO algorithms, where complementary search techniques enhance global exploration while preserving the rapid convergence property. Finally, the paper discusses theoretical aspects including convergence properties, computational complexity, and outlines current challenges and promising directions for future research.