Solving TSP with Simulated Annealing Algorithm

This article demonstrates how to solve the Traveling Salesman Problem (TSP) using the simulated annealing algorithm. TSP is an NP-hard problem that involves finding the shortest possible route for a salesman to visit each city exactly once and return to the starting city. We will explain the working mechanism and step-by-step implementation of the simulated annealing algorithm, including key components such as the energy function (total route distance), neighbor solution generation through city swaps, and the temperature cooling schedule. The implementation typically involves calculating route distances using Euclidean distance formulas, generating new solutions through random city permutations, and applying the Metropolis criterion for accepting suboptimal solutions. We will discuss parameter tuning strategies for initial temperature, cooling rate, and iteration counts to optimize solution quality. The article also includes performance comparisons with other optimization algorithms like genetic algorithms and ant colony optimization, highlighting convergence patterns and computational efficiency. Through this guide, you will learn practical implementation techniques for applying simulated annealing to TSP with Python/MatLAB code examples, gaining deep insights into probabilistic optimization and its real-world applications.