The Combination of Ant Colony Optimization (ACO) and Tabu Search (TS) Algorithm to Solve the Traveling Salesman Problem (TSP) Rico Wijaya Dewantoro, Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan, Indonesia ricowijayadewantoro@gmail.com Poltak Sihombing, Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan, Indonesia poltakhombing@yahoo.com Sutarman, Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan, Indonesia sutarman@usu.ac.id Abstract—In this research, the authors want to propose the combination of Ant Colony Optimization Algorithm and Tabu Search Algorithm as local search to solve Traveling Salesman Problem. This is a hybrid method of ACO to find best routes and get a better running time. One of the classic problems that can be used is TSP. In this research, the authors will compare the hybrid of ACO-TS and ACO. In this research, the hybrid of ACO-TS got the best routes and a better running time than ACO itself. It means that combination of ACO-TS is better than ACO itself. Therefore to get the best routes and a better running time, the author suggested the ACO-TS algorithm to solve TSP. Keywords— TSP, ACO, Tabu Search, Optimization, ACO-TS I. INTRODUCTION ACO algorithm has been a discussion from time to time to make this algorithm becomes more efficient in running time. ACO algorithm is one of the algorithm that usually used to solve TSP. ACO algorithm has been a topic of discussion because of its behavior [1]. The concept of ACO algorithm is modeled by the behavior of ants to find the best routes from the nest to the food sources. The method of ACO algorithm to solve the problem is based on the communication between the ants. When the ants go through the path, the ants will leave the footprints and these footprints called pheromone. Then this pheromone will become the information for another ant to reach the food sources. The shorter the path, the less occurrence the evaporation and this results in more pheromones left on the path. The ants will go through the path with the highest pheromones. The illustration of the ant colony finding the best routes can be seen in Fig.1. The ants will leave the footprints called pheromones on the way while traveling from the nest to food, in order to communicate with one to another to find the best routes. The ants have to make a decision that they should go left or right. The choice that is made is an erratic decision. The accumulation of pheromones is faster on the shorter pathway. The pheromones value changes from time to time makes the ants choose the shortest path with the highest pheromones [2], [3]. Fig. 1. The ant colony find the best routes The ACO algorithm has been the subject of discussion and attention from researchers since the 90s. ACO has solved various of optimization problems [3]. Most of the optimization problem can be solved using ACO. Even though ACO can solved most of the optimization problem, ACO algorithm has a weakness in running time where the ACO algorithm takes a long time to solve a problem. This problem becomes increasingly apparent when the weight of the problem given to the ACO algorithm increases. Because of that, there have been so many improvements that are made for the ACO algorithm [4]-[13]. But all the improvement only reached a certain limit. To improve the performance of the ACO algorithm, the authors used Tabu Search Algorithm as a local search. The results of the combination of ACO-TS Algorithm show that this combination can improve the performance of ACO algorithm. II. THE METHOD A. Traveling Salesman Problem (TSP) One of the well-known and widely studied problems in discrete optimization or combination is the Traveling Salesman Problem (TSP) [14]. The main problem of TSP is the journey of the salesman starting from the initial city to the next city and finally going back to the initial city. However, the rule is that every city other than the initial city can only be visited exactly 2019 The 3 rd International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM) 160 978-1-7281-2475-9/19/$31.00 ©2019 IEEE * *Corresponding author