Study And Application Of Improved Artificial Fish Swarm Algorithm

With the development of technology, Swarm intelligence optimization has played more and more significant role in the optimization problem. Artificial Fish Swarm Algorithm (AFSA) which based on swarm intelligence is an intelligent optimization method. An improved AFSA is presented after analyzing the disadvantages of AFSA. In order to improve the convergence rate of the increasing numbers of the fish school, the method of chaos are used to initializing the fish school.The precision of algorithm are increased by the improvement of the swarming behavior. In the behavior of preying, the strategy of dynamically which adjusting the parameter of step is presented in order to improve the convergence rate of the algorithm. Aiming at the constrained optimization problems, the paper introduces the separation method of constraint conditions. The results have indicated that the improved algorithm is effective. This improved algorithm is also applied in the optimization of the PID controller parameters. The simulation results show that this optimization algorithm is valid and effective.