An effective combination of Genetic Algorithms and the Variable Neighborhood Search for solving Travelling Salesman Problem

We investigate a mechanism of combining Genetic Algorithms (GA) and the Variable Neighborhood Search (VNS) for solving Traveling Salesman Problem (TSP). This combination aims to take advantage of GA´s strength in exploring solution space and VNS´s powerful exploitation in that space. In order to improve the solution quality, we propose a VNS with four neighborhood structures. We experimented on TSP instances from TSP-Lib and compared the results of the proposed algorithm with a hybrid Genetic Algorithm and Variable Neighborhood Search. Experimental results show that the proposed algorithm is better than once in terms of both the solution quality and running times in majority of data instances.