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 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.