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Durham University

Computer Science


Publication details for Professor Iain Stewart

Dawson, L. & Stewart, I.A. (2014), Accelerating ant colony optimization-based edge detection on the GPU using CUDA, 2014 IEEE Congress on Evolutionary Computation (CEC). Beijing, China, IEEE, Piscataway, NJ, 1736-1743.

Author(s) from Durham


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.