The Application of Multiobjective Genetic Algorithm to the Parameter Optimization of Single-Well Potential Stochastic Resonance Algorithm Aimed at Simultaneous Determination of Multiple Weak Chromatographic Peaks
release_n5zxtnxok5hvrpeztengpex26q
by
Haishan Deng, Shaofei Xie, Bingren Xiang, Ying Zhan, Wei Li, Xiaohua Li, Caiyun Jiang, Xiaohong Wu, Dan Liu
Abstract
Simultaneous determination of multiple weak chromatographic peaks via stochastic resonance algorithm attracts much attention in recent years. However, the optimization of the parameters is complicated and time consuming, although the single-well potential stochastic resonance algorithm (SSRA) has already reduced the number of parameters to only one and simplified the process significantly. Even worse, it is often difficult to keep amplified peaks with beautiful peak shape. Therefore, multiobjective genetic algorithm was employed to optimize the parameter of SSRA for multiple optimization objectives (i.e.,<jats:italic>S/N</jats:italic>and peak shape) and multiple chromatographic peaks. The applicability of the proposed method was evaluated with an experimental data set of Sudan dyes, and the results showed an excellent quantitative relationship between different concentrations and responses.
In application/xml+jats
format
Archived Files and Locations
application/pdf 1.9 MB
file_kp4obeve2raq3dyjfpmsbove6y
|
web.archive.org (webarchive) downloads.hindawi.com (publisher) |
application/pdf 1.0 MB
file_t7bt3nosdngjfbka6b5jipbnem
|
web.archive.org (webarchive) pdfs.semanticscholar.org (aggregator) |
Open Access Publication
In DOAJ
In ISSN ROAD
In Keepers Registry
ISSN-L:
1537-744X
access all versions, variants, and formats of this works (eg, pre-prints)
Crossref Metadata (via API)
Worldcat
SHERPA/RoMEO (journal policies)
wikidata.org
CORE.ac.uk
Semantic Scholar
Google Scholar