Teaching Learning Based Optimization

Teaching-Learning-Based Optimization (TLBO) is a metaheuristic optimization algorithm inspired by the teacher-student learning paradigm, aiming to efficiently find optimal solutions to complex problems. Current research focuses on enhancing TLBO's performance through hybridization with other algorithms (e.g., Grey Wolf Optimizer, Genetic Algorithms) and incorporating techniques like self-adjusting operators and optimized data sampling to improve convergence speed and solution quality. These advancements are proving valuable in diverse applications, including feature selection for text clustering, high-dimensional Bayesian optimization, and path planning for unmanned aerial vehicles, demonstrating TLBO's versatility and effectiveness in tackling challenging optimization tasks.