Using CUDA GPU to Accelerate the Ant Colony Optimization Algorithm

Wei, Kai-Cheng; Wu, Chao‐Chin; Wu, Chang-Chieh

doi:10.1109/pdcat.2013.21

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…While in works like [32,35,54,88], the behavior of GPU parallel implementations is not modified compared to the CPU implementations (quality not improved) but a significant acceleration factor of at best 696x [54] is achieved. Few works as [21,56,60,62,68,81] made the exception. They improve the quality along with keeping a high acceleration factor.…”

Section: Discussionmentioning

confidence: 99%

GPU parallelization strategies for metaheuristics: a survey

Essaid

Idoumghar

Lepagnot

et al. 2018

International Journal of Parallel, Emergent and Distributed Sys

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Metaheuristics have been showing interesting results in solving hard optimization problems. However, they become limited in terms of effectiveness and runtime for high dimensional problems. Thanks to the independency of metaheuristics components, parallel computing appears as an attractive choice to reduce the execution time and to improve solution quality. By exploiting the increasing performance and programability of graphics processing units (GPUs) to this aim, GPU-based parallel metaheuristics have been implemented using different designs. Recent results in this area show that GPUs tend to be effective co-processors for leveraging complex optimization problems. In this survey, mechanisms involved in GPU programming for implementing parallel metaheuristics are presented and discussed through a study of relevant research papers.

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Section: Discussionmentioning

confidence: 99%

GPU parallelization strategies for metaheuristics: a survey

Essaid

Idoumghar

Lepagnot

et al. 2018

International Journal of Parallel, Emergent and Distributed Sys

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show abstract

“…If using the local search (3-opt heuristic), the maximum speedup was 8.03x. In [32], based on the algorithm presented by Cecilia et al [5], Wei et al proposed an optimized ACO transition rule in which the maximum value was used instead of calculating the exact value of the sum of the probabilities. This resulted in further acceleration of this phase of the algorithm.…”

Section: Related Workmentioning

confidence: 99%

The GPU-based parallel Ant Colony System

Skinderowicz¹

2016

Journal of Parallel and Distributed Computing

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The Ant Colony System (ACS) is, next to Ant Colony Optimization (ACO) and the MAX-MIN Ant System (MMAS), one of the most efficient metaheuristic algorithms inspired by the behavior of ants. In this article we present three novel parallel versions of the ACS for the graphics processing units (GPUs). To the best of our knowledge, this is the first such work on the ACS which shares many key elements of the ACO and the MMAS, but differences in the process of building solutions and updating the pheromone trails make obtaining an efficient parallel version for the GPUs a difficult task. The proposed parallel versions of the ACS differ mainly in their implementations of the pheromone memory. The first two use the standard pheromone matrix, and the third uses a novel selective pheromone memory. Computational experiments conducted on several Travelling Salesman Problem (TSP) instances of sizes ranging from 198 to 2392 cities showed that the parallel ACS on Nvidia Kepler GK104 GPU (1536 CUDA cores) is able to obtain a speedup up to 24.29x vs the sequential ACS running on a single core of Intel Xeon E5-2670 CPU. The parallel ACS with the selective pheromone memory achieved speedups up to 16.85x, but in most cases the obtained solutions were of significantly better quality than for the sequential ACS.

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Cultural Ant Colony Optimization on GPUs for Travelling Salesman Problem

Unold

Tarnawski

2016

Lecture Notes in Computer Science

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Using CUDA GPU to Accelerate the Ant Colony Optimization Algorithm

Cited by 4 publications

References 8 publications

GPU parallelization strategies for metaheuristics: a survey

GPU parallelization strategies for metaheuristics: a survey

The GPU-based parallel Ant Colony System

Cultural Ant Colony Optimization on GPUs for Travelling Salesman Problem

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