Abstract
As a population-based algorithm, Ant Colony Optimization (ACO) is intrinsically massively parallel, and therefore it is expected to be well-suited for implementation on GPUs (Graphics Processing Units). In this paper, we present a novel ant colony optimization algorithm (called GACO), which based on Compute Unified Device Architecture (CUDA) enabled GPU. In GACO algorithm, we utilize some novel optimizations, such as hybrid pheromone matrix update, dynamic nearest neighbor path construction, and multiple ant colony distribution, which result in a higher speedup and a better quality solutions compared to other peer of algorithms. GACO is tested by the Traveling Salesman Problem (TSP) benchmark, and the experimental results show a total speedup up to 40:1× and 35:7× over implementation of Ant Colony System (ACS) and Max-min Ant System (MMAS), respectively.
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