The hybrid use of local search algorithms for the solution of the traveling salesman problems

In this thesis, the Traveling Salesman Problem (TSP) has been solved using the Local Search Optimization algorithm. The goal of TSP is to find the shortest route for a salesman to visit a specific group of cities, visiting each city only once and returning to the starting point. The local search algorithm has been employed to optimize TSP using methods such as 2-opt, 3-opt, Slide, and Swap. In local search, the 2-opt method aims to take an existing route and reorganize it without revisiting the same cities. The more complex 3-opt, although potentially slower than 2-opt, can yield better results. This study explores how TSP can be solved using the local search optimization algorithm. Furthermore, the performances of the 2-opt, 3-opt, Slide, and Swap methods have been analyzed and compared through experiments. To improve algorithm performance, the Local Search algorithm has been modified by randomly combining 30% 2-opt, 30% 3-opt, 20% Slide, and 20% Swap. This has reduced processing time and aimed to enhance the feasibility of problem resolution.