Dr. Magdalena Bialic-Davendra has a PhD in Finance from Tomas Bata University in Zlín, Czech Republic. She is author and co-author of more than thirty-five scientific articles. Her research interests include wildfire modeling, business clusters, operations research, and management science.
Part of the book: Traveling Salesman Problem
This research involves the development of a compute unified device architecture (CUDA) accelerated 2-opt local search algorithm for the traveling salesman problem (TSP). As one of the fundamental mathematical approaches to solving the TSP problem, the time complexity has generally reduced its efficiency, especially for large problem instances. Graphic processing unit (GPU) programming, especially CUDA has become more mainstream in high-performance computing (HPC) approaches and has made many intractable problems at least reasonably solvable in acceptable time. This chapter describes two CUDA accelerated 2-opt algorithms developed to solve the asymmetric TSP problem. Three separate hardware configurations were used to test the developed algorithms, and the results validate that the execution time decreased significantly, especially for the large problem instances when deployed on the GPU.
Part of the book: Novel Trends in the Traveling Salesman Problem