Teaching Learning Based Optimization Algorithm - MATLAB Implementation

This repository provides MATLAB source code for implementing the Teaching-Learning Based Optimization (TLBO) algorithm. Optimization algorithms are computational methods designed to find optimal solutions, widely applied across engineering, scientific research, and economic modeling domains. The TLBO algorithm specifically mimics the knowledge transfer process between teachers and students in a classroom setting to iteratively improve solution quality. The MATLAB implementation includes core functions for population initialization, teacher phase optimization, and learner phase enhancement. Key parameters such as population size, iteration count, and convergence criteria can be customized through configuration variables. The algorithm structure supports easy adaptation to various optimization problems by modifying the objective function definition and constraint handling mechanisms. For educational purposes, this codebase demonstrates fundamental optimization concepts including: - Population-based search strategies - Fitness evaluation procedures - Solution update mechanisms through teacher-student interactions - Convergence monitoring and termination conditions The implementation allows researchers and students to experiment with different problem configurations while maintaining the algorithm's original framework. Users can modify the objective function in the fitness evaluation module and adjust learning parameters to suit specific application requirements. This hands-on approach facilitates deeper understanding of optimization principles and their practical implementation in computational problem-solving.