Simulated Annealing Algorithm for TSP (Traveling Salesman Problem)

This document provides comprehensive information about implementing simulated annealing algorithm for solving the Traveling Salesman Problem (TSP). The simulated annealing algorithm mimics the crystal formation process during metal cooling, where solutions are randomly selected and accepted based on energy differences. This probabilistic approach helps escape local optima and improves solution efficiency. Key implementation aspects include: temperature scheduling functions that control the cooling rate, neighborhood generation methods for creating new routes, and acceptance probability calculations using the Metropolis criterion. The MATLAB implementation typically involves functions for distance matrix calculation, route cost evaluation, and temperature decay management. The algorithm workflow consists of initialization phase (setting initial temperature and solution), iteration phase (generating neighboring solutions and applying acceptance criteria), and convergence phase (gradual temperature reduction until termination conditions are met). Users can modify parameters like cooling rate, iteration limits, and neighborhood size to optimize performance for specific TSP instances. For practical implementation, the MATLAB source code provides customizable functions for route visualization, performance tracking, and solution validation, making it suitable for both educational purposes and research applications.