The Comparison of Genetic Algorithm and Ant Colony Optimization in Completing Travelling Salesman Problem Alexander 1 , Haris Sriwindono 2 alexanderujang96@gmail.com 1 , haris@usd.ac.id 2 Department of Informatics, Faculty of Science and Technology, Universitas Sanata Dharma, Yogyakarta, Indonesia Abstract. Traveling Salesman Problem abbreviated as TSP, is a NP-hard problem that is often applied in various applications. TSP is a polynomial problem, so the solution is exponential. One way to improve the resolution of NP-hard problems is to use probabilistic algorithms such as genetic algorithms, ant colony optimization algorithms, and others. In this study genetic algorithm (GA) was applied with ordered crossover method and reciprocal mutation method. And also use the ant colony algorithm (ACO). This research will compare the performance of the two algorithms. The data used are 10, 20, ..., 100 cities, so the result shows that the ant colony algorithm is able to find a shorter distance than the genetic algorithm, but the genetic algorithm shows a better speed of completion than the ant colony algorithm. Keywords : Travelling Salesman Problem, Genetic Algorithm, Ant Colonies Optimization 1 Introduction Traveling Salesman Problem (TSP) is a well-known optimization problem and is often used to test the performance of various algorithms. TSP itself is pretty much applied in the real world. The main problem of TSP is that a salesman must visit a number of cities, with the distance between cities already known beforehand. Each city can only be visited once and must return to the city of origin. Travel costs that are considered by the salesman can be in the form of distance, time, fuel, comfort, and so on. [1] Various methods are applied to handle TSP problems. The method is divided into two, namely the conventional method and the heuristic method. The conventional method is a method with ordinary mathematical calculations, while the heuristic method a technique designed for solving a problem more quickly when conventional methods are too slow, or for finding an approximate solution when classic methods fail to find any exact solution. This is achieved by trading optimality, completeness, accuracy, or precision for speed. In a way, it can be considered a shortcut. Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Soccer Games Optimization (SGO), are some examples of heuristic method algorithms for optimization [2][3]. ICSTI 2019, September 20, Yogyakarta, Indonesia Copyright © 2020 EAI DOI 10.4108/eai.20-9-2019.2292121