Travel Salesman Problem Algorithm Using Proposed Algorithm Development with K-Smallest City Method

Abstract

The Traveling Salesman Problem (TSP), classified as an NP-hard problem in combinatorial optimization, requires a lot of time to optimize large-scale data. In this paper, the proposed algorithm is designed to produce the smallest value and the fastest time in the computation process to find the shortest path. The results of the proposed algorithm design will be compared with algorithms that are generally widely used to solve TSP, including Ant Colony Optimization (ACO) and Brute Force, but in addition to comparing the results of the proposed algorithm in terms of minimum value and computational speed developed with the two algorithms, it will also be compared to the OR-Tools algorithm created by Google because to be able to evaluate the level of accuracy on the value generated on the large matrix dimension, it cannot be done on Brute Force because it takes a long time. The results show that the proposed algorithm can provide better results than ACO and shorter computation time than Brute Force even can still compete with Google's OR-Tools algorithm for the data used.