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Overview and Applications of Particle Swarm Optimization on GPGPU

by Sandeep U. Mane, Monica R. Pethkar, Pradnyarani K. Mahind
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 105 - Number 6
Year of Publication: 2014
Authors: Sandeep U. Mane, Monica R. Pethkar, Pradnyarani K. Mahind
10.5120/18383-9623

Sandeep U. Mane, Monica R. Pethkar, Pradnyarani K. Mahind . Overview and Applications of Particle Swarm Optimization on GPGPU. International Journal of Computer Applications. 105, 6 ( November 2014), 27-32. DOI=10.5120/18383-9623

@article{ 10.5120/18383-9623,
author = { Sandeep U. Mane, Monica R. Pethkar, Pradnyarani K. Mahind },
title = { Overview and Applications of Particle Swarm Optimization on GPGPU },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 105 },
number = { 6 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 27-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume105/number6/18383-9623/ },
doi = { 10.5120/18383-9623 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:37:01.510276+05:30
%A Sandeep U. Mane
%A Monica R. Pethkar
%A Pradnyarani K. Mahind
%T Overview and Applications of Particle Swarm Optimization on GPGPU
%J International Journal of Computer Applications
%@ 0975-8887
%V 105
%N 6
%P 27-32
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Particle Swarm Optimization is robust and effective method to solve optimization problems. Particle Swarm Optimization takes more time to find optimal solutions for complex real world problems. Execution time required to find optimal solutions depends on nature of problem as well as population and dimension size of the application. Compute intensive problems can be solved efficiently on General Purpose Graphics Processing Unit using Particle Swarm Optimization to diminish processing time. Graphics Processing Unit is used to provide speedup and to find optimal solutions of compute intensive problems earlier than central processing unit. Particle Swarm Optimization has eased to parallelize on Graphics Processing Unit using CUDA. This paper's main contribution is the review of parallelization techniques for Particle Swarm Optimization, performance optimization strategies and brief about different applications solved using Particle Swarm Optimization on GPGPU.

References
  1. Kennedy, J. and Russell, E. 1995. Particle swarm optimization. In Proceeding of IEEE International Conference on Neural Networks.
  2. NVidia. CUDA-C Programming Guide version 5. 1. 2013.
  3. Top Ten Review. Top ten graphics cards list, 25 August 2013. http://graphics-cards-review. toptenreviews. com.
  4. Umbarkar, A. J. , Joshi, M. S. , and Rothe, N. M. 2013. Genetic algorithm on general purpose graphical processing unit: Parallelism review. J. ICTACT Soft Computing. 3. 492–497.
  5. Zhou, Y. and Tan, Y. 2011. GPU-based parallel multiobjective particle swarm optimization. J. Artificial Intelligence. 7(A11). 125–141.
  6. Majd, A. , and Sahebi, G. 2014. A Survey on Parallel Evolutionary Computing and Introduce Four General Frameworks to Parallelize All EC Algorithms and Create New Operation for Migration. J. Information and Computing Science, 9(2), 097-105.
  7. Miguel, C. M. , Miguel, A. , Rodrguez-Vazquez, J. J. , and Antonio, G. I. 2011. Accelerating particle swarm algorithm with GPGPU. In IEEE 19th International Euro micro Conference on Parallel, Distributed and Network-Based Processing.
  8. Mussi, L. , Stefano, C. , and Daolio, F. 2009. GPU-based road sign detection using particle swarm optimization. In 9th IEEE International Conference on Intelligent Systems Design and Applications.
  9. Wenna, L. and Zhenyu. Z. 2011. A CUDA-based multi-channel particle swarm algorithm. In 4th International Conference on Control, Automation and Systems Engineering.
  10. Zhou, Y. and Tan, Y. 2009. GPU-based parallel particle swarm optimization. In IEEE Congress on Evolutionary Computation.
  11. Zhu, H. and Guo, Y. 2011. Paralleling Euclidean particle swarm optimization in CUDA. In 4th International Conference on Intelligent Networks and Intelligent Systems.
  12. Calazan, R. D. M. , Nedjah, N. , and Mourelle, L. D. M. 2013. A Cooperative Parallel Particle Swarm Optimization for High-Dimension Problems on GPUs. In IEEE Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI & CBIC).
  13. Papadakis, S. E. , and Bakrtzis, A. G. 2011. A GPU accelerated PSO with application to economic dispatch problem. In 16th IEEE International Conference on Intelligent System Application to Power Systems (ISAP).
  14. Veronese, L. P. , and Krohling, R. A. 2009. Swarms flight: Accelerating the particles using C-CUDA. In IEEE Congress on Evolutionary Computation.
  15. Hsieh, H. T. , and Chu, C. H. 2010. GPU-based optimization of tool path planning in 5-axis flank milling. In IEEE International Conference on Manufacturing Automation.
  16. Platos, J. , Snasel, V. , Jezowicz, T. , Kromerand, P. , and Abraham, A. 2012. A PSO-based document classification algorithm accelerated by the CUDA platform. In IEEE International Conference on Systems, Man and Cybernetics.
  17. Zhang, B. , Zheng, H. ,Wei, M. , Wu, R. , and Sheng, X. 2012. Particle swarm optimization of frequency selective surface. In IEEE International Conference on Cross Strait Quad- Regional Radio Science and Wireless Technology.
  18. Bastos-Filho, C. J. , Oliveira, M. A. , Nascimento, D. N. , and Ramos, A. D. 2010. Impact of the random number generator quality on particle swarm optimization algorithm running on graphic processor units. In 10th IEEE International Conference on Hybrid Intelligent Systems (HIS).
  19. Jambhlekar, P. A. , Mishra, M. , and Subramaniam, S. V. 2011. Parallel implementation of MOPSO on GPU using OpenCL and CUDA. In 18th IEEE International Conference on High Performance Computing (HiPC).
  20. Chang, Y. , L. , and Fang, J. P. 2009. Band selection for hyperspectral images based on parallel particle swarm optimization schemes. In IEEE International Geoscience and Remote Sensing Symposium.
  21. Zhu, W. , and Curry, J. 2009. Particle swarm with graphics hardware acceleration and local pattern search on bound constrained problems. In IEEE Swarm Intelligence Symposium.
  22. Sharma, B. , Thulasiram, R. K. , and Thulasiraman, P. 2012. Portfolio management using particle swarm optimization on GPU. In 10th IEEE International Symposium on Parallel and Distributed Processing with Applications.
  23. Javier, R. S. , and Julio, M. H. 2012. High performance GBC based particle swarm optimization for orthorectification of airborne push broom imagery. In IEEE International Geoscience and Remote Sensing Symposium.
  24. Rabinovich, M. , Kainga, P. , Johnson, D. , Shafer, B. , Lee, J. , J. , and Eberhart, R. 2012. Particle swarm optimization on a GPU. In IEEE International Conference on Electro/Information Technology.
  25. Kromer, P. , Platos, J. , and Snasel, V. 2013. A brief survey of advances in particle swarm optimization on graphic processing units. In IEEE World Congress on Nature and Biologically Inspired Computing (NaBIC).
  26. Mussi, L. , Daolio, F. , and Cagnoni, S. 2011. Evaluation of parallel particle swarm optimization algorithms within the CUDA architecture. J. Information Sciences. 181. 4642–4657.
  27. Roberge, V. , and Tarbouchi, M. 2012. Efficient parallel particle swarm optimizers on GPU for real-time harmonic minimization in multilevel inverters. In 38th IEEE Annual Conference on Industrial Electronics Society (IECON).
  28. Ugolotti, R. , Nashed, Y. S. , Mesejo, P. , Ivekovic, S. , Mussi, L. , and Cagnoni, S. 2013. Particle Swarm Optimization and Differential Evolution for model-based object detection. J. Applied Soft Computing, 13(6). 3092-3105.
  29. Altinoz, O. T. , Yilmaz, A. E. , and Ciuprina, G. 2013. Impact of problem dimension on the execution time of parallel particle swarm optimization implementation. In 8th IEEE International Symposium on Advanced Topics in Electrical Engineering (ATEE).
  30. Li, Y. , Xing, Y. , Gosalvez, M. A. , Pal, P. , and Zhou, Y. 2013. Particle swarm optimization of model parameters: Simulation of deep reactive ion etching by the continuous cellular automaton. In The 17th IEEE International Conference on Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS & EUROSENSORS XXVII).
  31. Calazan, R. M. , Nedjah, N. , and Mourelle, L. M. 2014. A hardware accelerator for Particle Swarm Optimization. J. Applied Soft Computing. 14. 347-356.
  32. Blecic, I. , Cecchini, A. , and Trunfio, G. A. 2014. Fast and Accurate Optimization of a GPU-accelerated CA Urban Model through Cooperative Coevolutionary Particle Swarms. Procedia Computer Science. 29.
  33. Van Heerden, K. , Fujimoto, Y. , and Kawamura, A. 2014. A combination of particle swarm optimization and model predictive control on graphics hardware for real-time trajectory planning of the under-actuated nonlinear Acrobot. In 13th IEEE International Workshop on Advanced Motion Control (AMC).
  34. Ma, J. , Man, K. L. , Ting, T. O. , Zhang, N. , Guan, S. U. , and Wong, P. W. 2014. Accelerating Parameter Estimation for Photovoltaic Models via Parallel Particle Swarm Optimization. In IEEE International Symposium on Computer, Consumer and Control (IS3C).
Index Terms

Computer Science
Information Sciences

Keywords

General Purpose Graphics Processing Unit (GPGPU) Swarm Intelligence

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