Optimization of PID controller parameters based on an improved artificial fish swarm algorithm

Abstract

The artificial fish swarm algorithm is a new kind of optimizing method based on the model of autonomous animals. After analyzing the disadvantages of AFSA, an improved artificial fish swarm algorithm is presented. According to the ergodicity and stochasticity of chaos, the basic AFSA is combined with chaos in order to initialize the fish school. The improvement of the swarming behavior increased the precision of the algorithm. In the behavior of preying, the strategy of dynamically adjusting the parameter of step is presented in order to improve the convergence rate of the algorithm. This improved AFSA is applied in the optimization of the of PID controller parameters. The simulation results show that this improved AFSA algorithm is effective and better than the basic AFSA algorithm.