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Attract-Repulse Fireworks Algorithm and its CUDA Implementation Using Dynamic Parallelism

Attract-Repulse Fireworks Algorithm and its CUDA Implementation Using Dynamic Parallelism

Ke Ding, Ying Tan
Copyright: © 2015 |Volume: 6 |Issue: 2 |Pages: 31
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781466678286|DOI: 10.4018/IJSIR.2015040101
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MLA

Ding, Ke, and Ying Tan. "Attract-Repulse Fireworks Algorithm and its CUDA Implementation Using Dynamic Parallelism." IJSIR vol.6, no.2 2015: pp.1-31. http://doi.org/10.4018/IJSIR.2015040101

APA

Ding, K. & Tan, Y. (2015). Attract-Repulse Fireworks Algorithm and its CUDA Implementation Using Dynamic Parallelism. International Journal of Swarm Intelligence Research (IJSIR), 6(2), 1-31. http://doi.org/10.4018/IJSIR.2015040101

Chicago

Ding, Ke, and Ying Tan. "Attract-Repulse Fireworks Algorithm and its CUDA Implementation Using Dynamic Parallelism," International Journal of Swarm Intelligence Research (IJSIR) 6, no.2: 1-31. http://doi.org/10.4018/IJSIR.2015040101

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Abstract

Fireworks Algorithm (FWA) is a recently developed Swarm Intelligence Algorithm (SIA), which has been successfully used in diverse domains. When applied to complicated problems, many function evaluations are needed to obtain an acceptable solution. To address this critical issue, a GPU-based variant (GPU-FWA) was proposed to greatly accelerate the optimization procedure of FWA. Thanks to the active studies on FWA and GPU computing, many advances have been achieved since GPU-FWA. In this paper, a novel GPU-based FWA variant, Attract-Repulse FWA (AR-FWA), is proposed. AR-FWA introduces an efficient adaptive search mechanism (AFW Search) and a non-uniform mutation strategy for spark generation. Compared to the state-of-the-art FWA variants, AR-FWA can greatly improve the performance on complicated multimodal problems. Leveraging the edge-cutting dynamic parallelism mechanism provided by CUDA, AR-FWA can be implemented on the GPU easily and efficiently.

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