MATLAB Implementation of Tabu Search Algorithm for Traveling Salesman Problem

Tabu Search Algorithm is an efficient heuristic optimization method frequently employed to solve the Traveling Salesman Problem (TSP). By incorporating a tabu list and aspiration criteria, the algorithm avoids local optima traps and conducts more effective global searches within the solution space. Core Algorithm Components Initial Solution Generation: Typically created using random generation or greedy strategy to construct initial paths. In MATLAB implementation, this can be achieved through functions like randperm for random path generation or nearest-neighbor heuristic for greedy initialization. Neighborhood Operations: Candidate solutions are generated through operations such as swap, insertion, or reversal, with corresponding path length calculations. The code typically implements these operations using array manipulation functions and maintains a candidate solution pool for evaluation. Tabu List Management: Records recent operations to prevent repeated searches, with Tabu Tenure parameters controlling solution diversity. The MATLAB implementation uses dynamic matrix updates to track forbidden moves and their expiration iterations. Aspiration Criteria: Allows acceptance of superior solutions even if they're tabu-active when they outperform historical best solutions, ensuring algorithm convergence. This is implemented through conditional checks comparing current solution quality with global best. Termination Conditions: Usually maximum iteration count or prolonged periods without solution improvement. The code includes loop controls with break conditions based on iteration counters and solution quality metrics. MATLAB Implementation Key Points Uses matrices to store city coordinates and distance matrices, with vectorized operations for efficient distance calculations. Implements short-term memory functionality through dynamically updated tabu lists using cell arrays or matrices. Includes visualization modules that display path optimization progress in real-time using plot functions and animation tools. Extended Optimization Directions Adaptive Tabu Tenure combined with simulated annealing principles for dynamic parameter adjustment. Parallel computing implementation to accelerate neighborhood evaluation using MATLAB's Parallel Computing Toolbox. Variable Neighborhood Search (VNS) integration to enhance global exploration capabilities through multiple neighborhood structures.