Hybrid Genetic Algorithm and Ant Colony Algorithm for Solving Travelling Salesman Problem - Thongpiem, Jirawat; Punkong, Narong; Ratanavilisagul, Chiabwoot; Kosolsombat, Somkiat (2024 IEEE 9th International Conference on Computational Intelligence and Applications (ICCIA), 2024)
The traveling salesman problem (TSP) is a combinatorial optimization problem and a NP-complete problem. It is a well-known problem for comparison of algorithm performance. Many researchers try to solve the TSP by the meta-heuristic algorithms. Ant Colony Optimization (ACO) algorithm is a popular method to solve the TSP. To enhance performance of ACO for solving TSP, the recently proposed improving the ACO algorithm by the results from searching for 2-OPT algorithms that are applied with pheromones of ants. Moreover, when the population of ACO occurs trapping in local optimum, the pheromone of ants is re-initialized to solve trapping in local optimum problem. This algorithm is called Modified Ant Colony Optimization with updating Pheromone by Leader and Re-initialization Pheromone (MACO-LR). However, performance searching of MACO-LR can be improved. In this research, we propose to solve TSP using Hybrid Genetic Algorithm and Ant Colony Algorithm (HGAACO) to optimize the total travelled distance. The proposed technique is tested on twenty-three maps from the Traveling Salesman Problem Library (TSPLIB) and it is composed with MACO-LR. The results from the experiment show the proposed technique can enhance performance searching of MACO-LR.