Simulated annealing algorithm based on MATLAB solves the traveling salesman problem in 34 cities
The Traveling Salesman Problem (TSP) is a classic combinatorial optimization problem. Its goal is to find the shortest path that allows the traveling salesman to visit a given set of cities in sequence and return to the starting city, while each city can only be visited once. In this article, we will use MATLAB to write code to solve the 34-city traveling salesman problem, using the simulated annealing algorithm.
The simulated annealing algorithm is a heuristic optimization algorithm that simulates the changes in atomic arrangement during the cooling of solid matter. By introducing an acceptance probability to accept inferior solutions, the simulated annealing algorithm can jump out of the local optimal solution in the search space and find the global optimal solution.
First, we need to define the coordinates of the 34 cities. Here we assume that the coordinates of the city have been given and are saved in a 34x2 matrix, where each row represents a city, the first column represents the abscissa, and the second column represents the ordinate.
cities = [x1, y1;
x2, y2;
...
x34, y34];
Next, we need to define how to calculate the distance between two cities