Dynamic Particle Swarm Optimization Algorithm for Resolution of Overlapping Chromatograms

Dynamic particle swarm optimization algorithm is proposed in this paper to resolve overlapping chromatographic peaks. To accelerate the convergence speed, clustering degree and evolution velocity are considered simultaneously to adjust inertia weight adaptively. The algorithm is tested on both simul...

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Bibliographic Details
Published in:2009 Fifth International Conference on Natural Computation Vol. 3; pp. 246 - 250
Main Author: Yufeng Li
Format: Conference Proceeding
Language:English
Published: IEEE 01.08.2009
Subjects:
Acceleration
Clustering algorithms
Convergence
Convolution
Curve fitting
Dynamic
Educational institutions
Gaussian distribution
Iterative algorithms
Overlapping Chromatograms
Particle swarm optimization
Resolution
Testing
ISBN:0769537367, 9780769537368
ISSN:2157-9555
Online Access:Get full text
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Summary:Dynamic particle swarm optimization algorithm is proposed in this paper to resolve overlapping chromatographic peaks. To accelerate the convergence speed, clustering degree and evolution velocity are considered simultaneously to adjust inertia weight adaptively. The algorithm is tested on both simulated overlapping chromatographic peaks which are based on exponential modified Gaussian convolution model and experimental overlapping chromatographic peaks of multi-component which includes brassicasterol, campesterol, stigmasterol and ß-sitosterol. Results indicate that the presented algorithm has fast convergence speed, high accuracy and reliability, and it can be used to resolve overlapping chromatographic peaks of multi-components.
ISBN:0769537367
9780769537368
ISSN:2157-9555
DOI:10.1109/ICNC.2009.370