Abstract
Today we are living in a world that is surrounded with information obesity which is also known as Big Data. Big data deals with zeta bytes of data flown from variety sources, and cannot be processed or analyzed using traditional procedure. Due to this, there is an increasing interest of researchers in using low cost GPUs for various applications that require intensive parallel computing to solve complex problems much faster. Various machine learning algorithms have been developed to obtain the optimal solutions with various data complexity. However, for big data problems, new machine learning algorithms need to be developed to deal with zeta bytes data problems. Centripetal accelerated particle swarm optimization (CAPSO) is the recent machine learning algorithm to enhance the convergence speed, accuracy and global optimality for optimization problems. However, the convergence speed of CAPSO is limited for small number of particles only. Hence, this research proposes improved CAPSO by implementing this algorithm on GPU platform through CUDA programming to handle N-dimensional scale of particles. Since CAPSO is intrinsically parallel processing, thus it can be effectively implemented on Graphics Processing Units (GPUs) according. The proposed GPU-based CAPSO was tested on various multi modal test functions and the results have proven that the proposed GPU-based CAPSO has successfully reduced the execution time with various particles dimensions compared to CPU-based CAPSO.
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Acknowledgements
The authors would like to thank the Universiti Teknologi Malaysia (UTM) for their support in Research and Development, UTM Big Data Centre and the Soft Computing Research Group (SCRG) for the inspiration in making this study a success. This work is supported by Ministry of Higher Education (MOHE) under Fundamental Research Grant Scheme (FRGS), Grant No. 4F802 and 4F786; and UTM under Research University Grant (RUG), Grant No. 17H62.
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Hasan, S., Bilash, A., Shamsuddin, S.M., Hassanien, A.E. (2018). GPU-Based CAPSO with N-Dimension Particles. In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_45
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DOI: https://doi.org/10.1007/978-3-319-74690-6_45
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