Accelerating ant colony optimization-based edge detection on the GPU using CUDA
Dawson, L.; Stewart, I.A.
Ant Colony Optimization (ACO) is a nature-inspired metaheuristic that can be applied to a wide range of optimization problems. In this paper we present the first parallel implementation of an ACO-based (image processing) edge detection algorithm on the Graphics Processing Unit (GPU) using NVIDIA CUDA. We extend recent work so that we are able to implement a novel data-parallel approach that maps individual ants to thread warps. By exploiting the massively parallel nature of the GPU, we are able to execute significantly more ants per ACO-iteration allowing us to reduce the total number of iterations required to create an edge map. We hope that reducing the execution time of an ACO-based implementation of edge detection will increase its viability in image processing and computer vision.
Dawson, L., & Stewart, I. (2014). Accelerating ant colony optimization-based edge detection on the GPU using CUDA. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation : July 6-11, 2014, Beijing, China (1736-1743). https://doi.org/10.1109/cec.2014.6900638
|Conference Name||2014 IEEE Congress on Evolutionary Computation (CEC)|
|Conference Location||Beijing, China|
|Start Date||Jul 6, 2014|
|End Date||Jul 11, 2014|
|Publication Date||Jul 1, 2014|
|Deposit Date||Dec 4, 2014|
|Publicly Available Date||Aug 8, 2016|
|Book Title||Proceedings of the 2014 IEEE Congress on Evolutionary Computation : July 6-11, 2014, Beijing, China.|
|Related Public URLs||http://community.dur.ac.uk/i.a.stewart/Papers/AcceleratingACO.pdf|
Accepted Conference Proceeding
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