Optimization design for tandem cascades of compressors based on adaptive particle swarm optimization

To improve the flow performance of tandem cascades on design and off design incidence angle and increase the stable operation range, an optimization system for tandem cascades was developed based on an adaptive particle swarm optimization (APSO-PDC). Firstly, APSO-PDC was proposed based on adaptive selection of particle roles and population diversity control. The adaptive selection of particle roles which combines the evolutionary state and dynamic particle state estimation (DPSE) method will sort the particles into three roles to help different particles execute different search tasks during optimization process. The population diversity control, which combines comprehensive learning strategy of the comprehensive learning particle swarm optimizer (CLPSO) with evolutionary state, pretty strengthens the exploration ability and avoids falling into the local optima. The performance of APSO-PDC is evaluated by 11 unimodal and multimodal functions. Compared with the other six PSOs, the results indicate APSO-PDC has better performance in terms of algorithm accuracy and algorithm reliability. In addition, APSO-PDC is validated by optimizing two large-turning tandem cascades, including low-dimension (5 optimization variables) and high-dimension problems (34 optimization variables). Compared with the other six PSOs, the optimization results demonstrate APSO-PDC has the fastest convergence speed and simultaneously controls well the population diversity.