Solution for TSP/MTSP with an improved parallel clustering and elitist ACO

Many problems that were considered complex and unsolvable have started to
 solve and new technologies have emerged through to the development of GPU
 technology. Solutions have established for NP-Complete and NP-Hard problems 
 with the acceleration of studies in the field of artificial intelligence,
 which are very interesting for both mathematicians and computer scientists.
 The most striking one among such problems is the Traveling Salesman Problem
 in recent years. This problem has solved by artificial intelligence?s
 metaheuristic algorithms such as Genetic algorithm and Ant Colony
 optimization. However, researchers are always looking for a better solution.
 In this study, it is aimed to design a low-cost and optimized algorithm for
 Traveling Salesman Problem by using GPU parallelization, Machine Learning,
 and Artificial Intelligence approaches. In this manner, the proposed algorithm consists of three stages; Cluster the points in the given dataset with
 K-means clustering, find the shortest path with Ant Colony in each of the
 clusters, and connect each cluster at the closest point to the other. These
 three stages were carried out by parallel programming. The most obvious
 difference of the study from those found in the literature is that it
 performs all calculations on the GPU by using Elitist Ant Colony
 Optimization. For the experimental results, examinations were carried out
 on a wide variety of datasets in TSPLIB and it was seen that the proposed 
 parallel KMeans-Elitist Ant Colony approach increased the performance by 30%
 compared to its counterparts.