MATLAB Implementation of Tabu Search Algorithm for TSP Problem

This article presents an effective solution for the Traveling Salesman Problem (TSP) using the Tabu Search algorithm. The MATLAB implementation demonstrates how to approach complex optimization problems by maintaining a tabu list to prevent cycling and exploring non-improving moves through strategic aspiration criteria. The algorithm incorporates key components including neighbor solution generation using 2-opt or exchange operations, fitness evaluation through tour distance calculation, and dynamic tabu list management with fixed-size FIFO structure. For beginners, this implementation provides practical insights into metaheuristic optimization techniques, showcasing how to balance intensification (using best-found solutions) and diversification (exploring new search areas) strategies. The code structure clearly separates problem initialization, solution representation, move operators, and termination conditions, making it an excellent educational resource for understanding algorithm customization and parameter tuning in combinatorial optimization problems. The implementation not only solves practical TSP instances but also enhances understanding of algorithm optimization principles, including solution encoding as permutation vectors, objective function computation using Euclidean distance matrices, and adaptive search strategies that make tabu search particularly effective for large-scale optimization problems.