Parallelization Strategies for GPU-Based Ant Colony Optimization Applied to TSP

Abstract

Graphics Processing Units (GPUs) have been widely used to speed up the execution of various meta-heuristics for solving hard optimization problems. In the case of Ant Colony Optimization (ACO), many implementations with very distinct parallelization strategies and speedups have been already proposed and evaluated on the Traveling Salesman Problem (TSP). On the one hand, a coarse-grained strategy applies the parallelization on the ant-level and is the most intuitive and common strategy found in the literature. On the other hand, a fine-grained strategy also parallelizes the internal work of each ant, creating a higher degree of parallelization. Although many parallel implementations of ACO exist, the influence of the algorithm parameters (e.g., the number of ants) and the problem configurations (e.g., the number of nodes in the graph) on the performance of coarse- and fine-grained parallelization strategies has not been investigated so far. Thus, this work performs a series of experiments and provides speedup analyses of two distinct ACO parallelization strategies compared to a sequential implementation for different TSP configurations and colony sizes. The results show that the considered factors can significantly impact the performance of parallelization strategies, particularly for larger problems. Furthermore, we provide a recommendation for the parallelization strategy and colony size to use for a given problem size and some insights for the development of other GPU-based meta-heuristics.