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The Journal of Supercomputing

Erschienen in:

01.04.2015

Gravitational search algorithm using CUDA: a case study in high-performance metaheuristics

verfasst von: Amirreza Zarrabi, Khairulmizam Samsudin, Ettikan K. Karuppiah

Erschienen in: The Journal of Supercomputing | Ausgabe 4/2015

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Abstract

Many scientific and technical problems with massive computation requirements could benefit from the graphics processing units (GPUs) using compute unified device architecture (CUDA). Gravitational search algorithm (GSA) is a population-based metaheuristic which can be effectively implemented on GPU to reduce the execution time. Nonetheless, the performance improvement depends strongly on the process used to adapt the algorithm into CUDA environment. In this paper, we discuss possible approaches to parallelize GSA on graphics hardware using CUDA. An in-depth study of the computation efficiency of parallel algorithms and capability to effectively exploit the architecture of GPU is performed. Additionally, a comparative study of parallel and sequential GSA was carried out on a set of standard benchmark optimization functions. The results show a significant speedup while maintaining results quality which re-emphasizes the utility of CUDA-based implementation for complex and computationally intensive parallel applications.

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Alba E, Luque G (2006) Evaluation of parallel metaheuristics. Lect Notes Comput Sci 4193:9–14

Calazan RM, Nedjah N, de Macedo Mourelle L (2012) Swarm grid: a proposal for high performance of parallel particle swarm optimization using GPGPU. In: Computational science and its applications-ICCSA 2012. Springer, Berlin, pp 148–160

Calazan RM, Nedjah N, de Macedo Mourelle L (2013) Parallel GPU-based implementation of high dimension particle swarm optimizations. In: IEEE Fourth Latin American Symposium on Circuits and systems (LASCAS), 2013, pp 1–4

Cárdenas-Montes M, Vega-Rodríguez MA, Rodríguez-Vázquez JJ, Gómez-Iglesias A (2012) A GPU-based evaluation to accelerate particle swarm algorithm. In: Computer aided systems theory-EUROCAST 2011. Springer, Berlin, pp 272–279

Charles JS, Potok TE, Patton R, Cui X (2008) Flocking-based document clustering on the graphics processing unit. Springer, BerlinCrossRef

Chen Z, Yuan X, Tian H, Ji B (2014) Improved gravitational search algorithm for parameter identification of water turbine regulation system. Energy Convers Manag 78:306–315CrossRef

de P Veronese L, Krohling RA (2009) Swarm’s flight: accelerating the particles using C-CUDA. In: IEEE Congress on Evolutionary computation, 2009, pp 3264–3270

Formato RA (2008) Central force optimization: a new nature inspired computational framework for multidimensional search and optimization. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2007). Springer, Berlin, pp 221–238

Gao S, Chai H, Chen B, Yang G (2013) Hybrid gravitational search and clonal selection algorithm for global optimization. In: Advances in swarm intelligence. Springer, Berlin, pp 1–10

10.

Glodberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addion wesley, USA

11.

Gotshall S, Rylander B (2008) Optimal population size and the genetic algorithm. Population 100(400):900

12.

Green II RC, Wang L, Alam M, Formato RA (2012) Central force optimization on a GPU: a case study in high performance metaheuristics. J Supercomput 62(1):378–398CrossRef

13.

Harris M (2007) Optimizing parallel reduction in CUDA. NVIDIA Developer Technology

14.

Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol 4, pp 1942–1948

15.

Kumar J, Singh L, Paul S (2013) GPU based parallel cooperative particle swarm optimization using C-CUDA: a case study. In: IEEE International Conference on Fuzzy Systems (FUZZ), 2013, pp 1–8

16.

Li J-M, Wang X-J, He R-S, Chi Z-X (2007) An efficient fine-grained parallel genetic algorithm based on GPU-accelerated. In: Proceedings of the 2007 IFIP International Conference on Network and Parallel Computing Workshops, pp 855–862

17.

Li J, Wan D, Chi Z (2007) An efficient fine-grained parallel particle swarm optimization method based on GPU-acceleration. Int J Innov Comput Inform Control 3(6):1707–1714

18.

Li-Ping Z, Huan-Jun Y, Shang-Xu H (2005) Optimal choice of parameters for particle swarm optimization. J Zhejiang Univ Sci A 6(6):528–534

19.

Mallick S, Ghoshal SP, Acharjee P, Thakur SS (2013) Optimal static state estimation using improved particle swarm optimization and gravitational search algorithm. Int J Electr Power Energy Syst 52:254–265CrossRef

20.

Mussi L, Daolio F, Cagnoni S (2011) Evaluation of parallel particle swarm optimization algorithms within the CUDA architecture. Inform Sci 181(20):4642–4657CrossRef

21.

Mussi L, Nashed YSG, Cagnoni S (2011) GPU-based asynchronous particle swarm optimization. In: Proceedings of the 13th annual conference on Genetic and evolutionary computation, pp 1555–1562. ACM

22.

CUDA Nvidia (2013) C programming guide 5.5. NVIDIA Corporation, USA

23.

Pedersen Magnus EH (2010) Good parameters for particle swarm optimization. Hvass Lab., Copenhagen, Denmark, Tech. Rep. HL1001

24.

Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inform Sci 179(13):2232–2248CrossRefMATH

25.

Rashedi E, Nezamabadi-Pour H, Saryazdi S (2011) Filter modeling using gravitational search algorithm. Eng Appl Artif Intell 24(1):117–122CrossRef

26.

Rong G, Liu G, Zheng M, Sun A, Tian Y, Wang H (2013) Parallel gravitation field algorithm based on the CUDA platform. J Inform Comput Sci 10:3635–3644CrossRef

27.

CUDA Toolkit (2013) Curand guide 5.5. NVIDIA Corporation, USA

28.

Wong M-L, Wong T-T, Fok K-L (2005) Parallel evolutionary algorithms on graphics processing unit. In: The IEEE Congress on Evolutionary Computation, 2005, vol 3, pp 2286–2293

29.

Zarrabi A, Samsudin K (2014) Task scheduling on computational grids using gravitational search algorithm. Cluster Comput 17(3):1001–1011

30.

Zhou Y, Tan Y (2009) Gpu-based parallel particle swarm optimization. In: IEEE Congress on Evolutionary Computation, 2009. CEC’09, pp 1493–1500

31.

Zhou Y, Tan Y (2010) Particle swarm optimization with triggered mutation and its implementation based on GPU. In: Proceedings of the 12th annual conference on Genetic and evolutionary computation, pp 1–8. ACM

Titel: Gravitational search algorithm using CUDA: a case study in high-performance metaheuristics
verfasst von: Amirreza Zarrabi
Khairulmizam Samsudin
Ettikan K. Karuppiah
Publikationsdatum: 01.04.2015
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 4/2015
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-014-1360-1

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