Implementing Simulated Annealing Algorithm with MATLAB: A Traveling Salesman Problem (TSP) Case Study

This article explains how to implement the simulated annealing algorithm using MATLAB, with detailed explanations based on the famous Traveling Salesman Problem (TSP). Simulated annealing is a stochastic search-based optimization algorithm suitable for solving various combinatorial optimization problems. The article covers the fundamental principles and step-by-step procedures of the simulated annealing algorithm, with specific focus on its application to solving TSP. Through practical implementation and optimization of simulated annealing for TSP instances, readers can better understand the algorithm's application and effectiveness. The implementation typically involves creating functions for initial route generation, cost calculation using distance matrices, neighbor solution generation through route perturbations (like 2-opt swaps), and temperature scheduling with exponential cooling. Key MATLAB functions such as randperm for initial solution generation and matrix operations for distance calculations will be discussed. The article also discusses the algorithm's advantages and limitations, along with potential improvement methods, enabling readers to better apply and adjust simulated annealing in practical applications. Specific optimization techniques like adaptive cooling schedules, reheating strategies, and hybrid approaches combining local search will be covered to enhance solution quality.