An Enhanced Particle Swarm Optimization Algorithm

Particle Swarm Optimization (PSO) algorithm is often used for finding optimal solution, but it easily entraps into the local extremum in later evolution period. Based on improved chaos searching strategy, an enhanced particle swarm optimization algorithm is proposed in this study. When particles get into the local extremum, they are activated by chaos search strategy, where the chaos search area is controlled in the neighborhood of current optimal solution by reducing search area of variables. The new algorithm not only gets rid of the local extremum effectively but also enhances the precision of convergence significantly. Experiment results show that the proposed algorithm is better than standard PSO algorithm in both precision and stability.

A kinetic model for nanocrystal morphology evolution

A kinetic model is developed with the goal of understanding and predicting the morphology evolution of nonocrystals in nonequilibrium growth conditions. The model is based on the assumption that under such conditions, different crystal planes have different kinetic parameters. This model focuses on the morphology-developing stage and is successfully related to the nucleation process and other crystal evolution mechanisms. It is believed to be a universal model and is applied to discuss the morphology evolution of CdSe nanocrystals, including the aspect ratio, injection I schemes, ligands effect and morphology distribution.