Simulated Annealing Algorithm for Neural Network-based TSP Implementation

This implementation solves the Traveling Salesman Problem (TSP) using neural networks optimized through simulated annealing algorithm in MATLAB. The solution employs a multi-layer neural network architecture to represent city connections and tour configurations, while the simulated annealing component helps escape local minima by probabilistically accepting worse solutions during the optimization process. Key implementation aspects include: - Designing a neural network structure with city coordinates as input and tour sequences as output - Implementing energy function calculation based on total tour distance - Creating annealing schedule with temperature decay mechanism - Developing state transition rules for neighbor solution generation through city swaps - Incorporating Boltzmann probability distribution for acceptance criteria The MATLAB code will feature functions for network initialization, distance matrix computation, temperature management, and convergence monitoring. Validation involves testing on standard TSP datasets with performance metrics including solution quality, convergence speed, and computational efficiency.