Accelerating ant colony optimization-based edge detection on the GPU using CUDA

Dawson, L.; Stewart, I.A.

Abstract

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.

Citation

Dawson, L., & Stewart, I. (2014, July). Accelerating ant colony optimization-based edge detection on the GPU using CUDA. Presented at 2014 IEEE Congress on Evolutionary Computation (CEC), Beijing, China

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2014 IEEE Congress on Evolutionary Computation (CEC)
• 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
• Pages: 1736-1743
• Series ISSN: 1089-778X
• Book Title: Proceedings of the 2014 IEEE Congress on Evolutionary Computation : July 6-11, 2014, Beijing, China.
• ISBN: 9781479914883
• DOI: https://doi.org/10.1109/cec.2014.6900638
• Public URL: https://durham-repository.worktribe.com/output/1153628
• Related Public URLs: http://community.dur.ac.uk/i.a.stewart/Papers/AcceleratingACO.pdf