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Evaluation of parallel particle swarm optimization algorithms within the CUDA architecture

Luca Mussi, Fabio Daolio, Stefano Cagnoni
Dipartimento di Ingegneria dell’Informazione, Universita degli Studi di Parma, Viale G. Usberti 181a, I-43124 Parma, Italy
Information Sciences (03 September 2010)

@article{mussi2010evaluation,

   title={Evaluation of Parallel Particle Swarm Optimization Algorithms within the CUDA Architecture},

   author={Mussi, L. and Daolio, F. and Cagnoni, S.},

   journal={Information Sciences},

   issn={0020-0255},

   year={2010},

   publisher={Elsevier}

}

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Particle swarm optimization (PSO), like other population-based meta-heuristics, is intrinsically parallel and can be effectively implemented on Graphics Processing Units (GPUs), which are, in fact, massively parallel processing architectures. In this paper we discuss possible approaches to parallelizing PSO on graphics hardware within the Compute Unified Device Architecture (CUDA), a GPU programming environment by nVIDIA which supports the company
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