Superheated steam temperature control based-on Cultural based Ant Colony optimization Algorithm for power station boiler

Cultural Algorithm (CA) model derived from the simulation of human being society evolution, provides a new computable framework of evolution algorithms, and different problems can use a different main population and knowledge space. This paper introduced Ant Colony Algorithm (ACA) into Cultural Algorithm framework, and put forward Cultural based Ant Colony Algorithm (C-ACA) model, and presented the design of upper belief space and the design of lower population space and the design of accepting function and influence function and algorithm steps. This algorithm will use dual evolution and dual improvement mechanism of Cultural Algorithm model to increase the diversity of the Ant Colony population and avoid search stagnation phenomenon. Compared with the Z-N optimization and GA optimization, this algorithm has higher operational precision and global search ability. Finally this algorithm is applied to PID controller parameter optimization for the superheated steam temperature of power station boiler. The results have shown the method validity and superior static and dynamic performance, and can satisfy the demand of Power Station Boiler.