A fast fine-grained genetic algorithm for spectrum fitting: An application to X-ray spectra

Brunetti, Antonio

doi:10.1016/j.cpc.2012.10.010

Cited by 8 publications

(2 citation statements)

References 31 publications

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“…The background has been extracted by using a orthonormal polynomial approach [24][25][26][27] while the deconvolution of the peaks has been performed using a genetic algorithm software, that allows very good performance in case of superimposed peaks. A fast Monte Carlo code has been also used in order to check the best experimental condition for rough surfaces [28,29].…”

Section: Methodsmentioning

confidence: 99%

Multilayered samples reconstructed by measuring Kα/Kβ or Lα/Lβ X-ray intensity ratios by EDXRF

Cesáreo

Assis

Roldán

et al. 2013

Nuclear Instruments and Methods in Physics Research Section B:

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Section: Methodsmentioning

confidence: 99%

Multilayered samples reconstructed by measuring Kα/Kβ or Lα/Lβ X-ray intensity ratios by EDXRF

Cesáreo

Assis

Roldán

et al. 2013

Nuclear Instruments and Methods in Physics Research Section B:

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“…Many parallel models like fine grained [28] , coarse grained [29] , and distributed models [30] were proposed for implementing parallel intelligent algorithms. However, most of the parallel models are based on MPI technology that is not the best choice for parallelization because of the weakness of handling the failure of nodes.…”

Section: Related Workmentioning

confidence: 99%

MR-IDPSO: a novel algorithm for large-scale dynamic service composition

Zhang

Jing

Zhang

2015

Tinshhua Sci. Technol.

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In the era of big data, data intensive applications have posed new challenges to the field of service composition. How to select the optimal composited service from thousands of functionally equivalent services but different Quality of Service (QoS ) attributes has become a hot research in service computing. As a consequence, in this paper, we propose a novel algorithm MR-IDPSO (MapReduce based on Improved Discrete Particle Swarm Optimization), which makes use of the improved discrete Particle Swarm Optimization (PSO) with the MapReduce to solve large-scale dynamic service composition. Experiments show that our algorithm outperforms the parallel genetic algorithm in terms of solution quality and is efficient for large-scale dynamic service composition. In addition, the experimental results also demonstrate that the performance of MR-IDPSO becomes more better with increasing number of candidate services.

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