Gezgin satıcı problemi çözümünde eniyileştirme algoritmalarının karşılaştırılması

The Theory of computational complexity is an essential branch of study in the science of theoretical computing and mathematics, the resolution of P and NP problems is one of the main problems that have open solutions, for which no famous efficient algorithm exist. The Problem of Traveling Salesman (TSP) is an example of these problems. In this problem, a count of specified cities must be visited by traveling salesman, starting and ending point is the same city. In the (TSP) the aim is to get a tour of all nodes so that the complete distance or time is minimized. The application of Evolutionary algorithms is one of the famous methods to solving problems of TSP. These algorithms are usually simulates naturally occurring phenomena in nature, which are employed in modeling algorithms of computer. Currently there exist several of such algorithms; for example, Optimization of Ant Colony (ACO) and Genetic Algorithm (GA). In this thesis, we analyzed the solution of TSP by GA and ACO and compared between the approaches after gathering solution results. The obtained results from our experiments showed that the ACO is better than GA since it requires less execution time for the same problem.