Multi-objective binary PSO with kernel P system on GPU

Naeimeh Elkhani, Ravie Chandren Muniyandi, Gexiang Zhang

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)323-336
Number of pages14
JournalInternational Journal of Computers, Communications and Control
Volume13
Issue number3
Publication statusPublished - 1 Jan 2018

Fingerprint

Particle swarm optimization (PSO)
Program processors
Feature extraction
Membranes
Costs
Graphics processing unit
Processing

Keywords

  • GPU based membrane computing
  • Kernel P system
  • Parallel kernel P system-multi objective binary PSO
  • Parallel membrane computing
  • Parallel multi-objective binary PSO

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications
  • Computational Theory and Mathematics

Cite this

Multi-objective binary PSO with kernel P system on GPU. / Elkhani, Naeimeh; Muniyandi, Ravie Chandren; Zhang, Gexiang.

In: International Journal of Computers, Communications and Control, Vol. 13, No. 3, 01.01.2018, p. 323-336.

Research output: Contribution to journalArticle

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