Research of PID parameter optimization based-on cultural based Ant Colony Algorithm for superheated steam temperature

摘要

Cultural Algorithm (CA) derived from the simulation of evolution of human being society, provides a new computable framework of evolution algorithms, and a different problem 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 (ACCA), and presented the design of lower population space and upper belief space. This method uses dual evolution and dual improvement mechanism of Cultural Algorithm Model to increase population diversity and avoid search stagnation phenomenon. Compared with the Z-N optimization and GA, 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 thermal power plant boiler. The results have shown the method validity and superior static ±2.5°C and dynamic ±5°C performance index. Moreover this algorithm and strategy discussed here has universality and can be applied to other optimization problems.