Traveling salesperson problem using Python

Abstract

A well-known optimization issue in operations research, mathematics, and computer science is the Traveling Salesman Problem (TSP). It entails determining the quickest path a salesman can take to travel to a series of cities, stop in each one exactly once, and then return to the beginning location. The issue is notoriously challenging because there are so many potential routes that they increase exponentially with the number of cities, rendering it unsolvable for a sizable number of cities. The TSP has been solved using a variety of algorithms, including heuristic and metaheuristic methods like genetic algorithms and simulated annealing, as well as accurate methods like branch and bound. Numerous industries, including manufacturing, logistics, and transportation, can use the TSP. By implementing Graph Neural Network (GNN) into TSP, it helps one to visualize the graph better as the edges and nodes are labelled with numbers