Multi-Objective Binary PSO with Kernel P System on GPU

  • Naeimeh Elkhani Centre for Cyber Security Faculty of Information Sciene and Technology Universiti Kebangsaan Malaysia 43600 Bangi, Selangor, Malaysia
  • Ravie Chandren Muniyandi Centre for Cyber Security Faculty of Information Sciene and Technology Universiti Kebangsaan Malaysia 43600 Bangi, Selangor, Malaysia
  • Gexiang Zhang School of Electrical Engineering Southwest Jiaotong University Chengdu 610031 Sichuan, P.R. China

Abstract

Computational cost is a big challenge for almost all intelligent algorithms which are run on CPU. In this regard, our proposed kernel P system multi-objective binary particle swarm optimization feature selection and classification method should perform with an efficient time that we aimed to settle via using potentials of membrane computing in parallel processing and nondeterminism. Moreover, GPUs perform better with latency-tolerant, highly parallel and independent tasks. In this study, to meet all the potentials of a membrane-inspired model particularly parallelism and to improve the time cost, feature selection method implemented on GPU. The time cost of the proposed method on CPU, GPU and Multicore indicates a significant improvement via implementing method on GPU.

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Published
2018-05-27
How to Cite
ELKHANI, Naeimeh; MUNIYANDI, Ravie Chandren; ZHANG, Gexiang. Multi-Objective Binary PSO with Kernel P System on GPU. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 13, n. 3, p. 323-336, may 2018. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/3282>. Date accessed: 04 july 2020. doi: https://doi.org/10.15837/ijccc.2018.3.3282.

Keywords

parallel membrane computing, GPU based membrane computing, kernel P system, parallel multi-objective binary PSO, parallel kernel P system-multi objective binary PSO