Gene-Ants: Ant Colony Optimization with Genetic Algorithm for Traveling Salesman Problem Solving
Sarin Thong-ia, Paskorn Champrasert
Abstract
The Traveling Salesman Problem (TSP) is a well-known NP-hard problem that receives attention in many fields. To solve the NP-hard problems, Ant Colony Optimization (ACO) is a popular meta-heuristic that gives an effective solution of TSP but the limitation of ACO has an early stage of optimization and falls to the local optimal. To overcome the limitation of ACO, we use Genetic Algorithm (GA) that has the ability to avoid local optimal for improving the solution of ACO. In this paper, we present the ACO with the genetic operation from GA that is called Gene-Ants. Also, compare the result of the Gene-Ants algorithm with the basic ACO algorithm in the different TSP instances benchmark. The summarized results from the experiments show the Gene-Ants algorithm outperforms the basic ACO algorithm in terms of global optimal solution finding and convergence rate.