Efficient local search on the GPU-Investigations on the vehicle routing problem
Journal of Parallel and Distributed Computing
Parallel multi-objective Ant Programming for classification using GPUs
Journal of Parallel and Distributed Computing
Parallel ant colony optimisation algorithm for continuous domains on graphics processing unit
International Journal of Computing Science and Mathematics
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Ant Colony Optimisation (ACO) is an effective population-based meta-heuristic for the solution of a wide variety of problems. As a population-based algorithm, its computation is intrinsically massively parallel, and it is therefore theoretically well-suited for implementation on Graphics Processing Units (GPUs). The ACO algorithm comprises two main stages: textit{Tour construction} and textit{Pheromone update}. The former has been previously implemented on the GPU, using a task-based parallelism approach. However, up until now, the latter has always been implemented on the CPU. In this paper, we discuss several parallelisation strategies for {it both} stages of the ACO algorithm on the GPU. We propose an alternative {it data-based} parallelism scheme for textit{Tour construction}, which fits better on the GPU architecture. We also describe novel GPU programming strategies for the textit{Pheromone update} stage. Our results show a total speed-up exceeding 28x for the textit{Tour construction} stage, and 20x for textit{Pheromone update}, and suggest that ACO is a potentially fruitful area for future research in the GPU domain.