Solving TSP Problem with 50 Cities Using Tabu Search Algorithm

In this document, I will demonstrate how to solve the Traveling Salesman Problem (TSP) involving 50 cities using the Tabu Search algorithm. Tabu Search is a metaheuristic optimization algorithm particularly effective for solving complex combinatorial problems like TSP. The algorithm employs a "Tabu List" to record recent moves, preventing cycles and helping escape local optima by temporarily forbidding previously visited solutions. For beginners, Tabu Search offers an accessible implementation approach with clear components: 1. Solution representation typically as an array of city indices 2. Neighborhood generation through swap or inversion operations 3. Tabu list management using FIFO (First-In-First-Out) structure 4. Aspiration criteria to override tabu status when finding better solutions Key implementation aspects include: - Initial solution generation via random permutation or greedy methods - Fitness function calculating total tour distance - Move evaluation comparing candidate solutions - Termination conditions based on iterations or convergence The algorithm proves highly practical for real-world problems like TSP, logistics routing, and scheduling optimization. Code typically involves matrix operations for distance calculations and list manipulations for tabu management, making it suitable for programming languages like Python, MATLAB, or Java.