TSP Algorithm Implementation Using Simulated Annealing Approach

This implementation provides a Simulated Annealing-based solution for the Traveling Salesman Problem (TSP). The algorithm's core mechanism involves simulating the annealing process of metals, where gradual cooling allows molecules to settle into low-energy states. In code terms, this translates to: initializing random routes, generating neighbor solutions through operations like 2-opt swaps or node reordering, and accepting worse solutions probabilistically based on a temperature parameter that decreases over iterations. Key implementation aspects include: - Temperature scheduling using exponential decay functions - Energy calculation through total route distance computation - Acceptance probability using the Metropolis criterion: exp(-ΔE/T) - Convergence criteria based on iteration counts or temperature thresholds For comprehensive algorithm explanations and code walkthrough, please consult the included tutorial documentation. Should you require more detailed tutorials or high-resolution materials, kindly contact me via email at 1066146635@qq.com. I would be pleased to provide additional assistance and support for understanding this optimization approach.