11162

Candidate set parallelization strategies for Ant Colony Optimization on the GPU

Laurence Dawson, Iain A. Stewart
School of Engineering and Computing Sciences, Durham University, Durham, United Kingdom
13th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP’13), 2013

@incollection{dawson2013candidate,

   title={Candidate set parallelization strategies for Ant Colony Optimization on the GPU},

   author={Dawson, Laurence and Stewart, Iain A},

   booktitle={Algorithms and Architectures for Parallel Processing},

   pages={216–225},

   year={2013},

   publisher={Springer}

}

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For solving large instances of the Travelling Salesman Problem (TSP), the use of a candidate set (or candidate list) is essential to limit the search space and reduce the overall execution time when using heuristic search methods such as Ant Colony Optimisation (ACO). Recent contributions have implemented ACO in parallel on the Graphics Processing Unit (GPU) using NVIDIA CUDA but struggle to maintain speedups against sequential implementations using candidate sets. In this paper we present three candidate set parallelization strategies for solving the TSP using ACO on the GPU. Extending our past contribution, we implement both the tour construction and pheromone update stages of ACO using a data parallel approach. The results show that against their sequential counterparts, our parallel implementations achieve speedups of up to 18x whilst preserving tour quality.
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