Improvement Of Artificial Fish Swarm Algorithm And Application In Parameter Estimation For An Induction Motor's Model

Artificial Fish Swarm Algorithm (AFSA) is a new intelligent optimization method and has been applied widely. But there are limitations. After analyzing the optimization theory of AFSA, finding problems and studying improved AFSA having been presented before, an improved Artificial Fish Swarm Algorithm based on simplex method (SMIAFSA) is proposed. In the latter evolution period the improved algorithm uses simplex method operators that are distributed evenly and widely as its artificial fishes to replace the original fishes that get together round the local optimum solution in every some generations. By adding simplex method operators to artificial fish swarm algorithm, the level of detailed search is greatly improved in local part. It dynamically adjusts the vision and step of artificial fish to keep the balance between global and local search ability. And it simplifies the searching food behavior to save the running time. Finally, the improved algorithm has been proved to be a more effective algorithm in solving the problem of the low optimization accuracy by using three typical test functions. And it has been proved it can estimate the parameters of an induction motor's static and dynamic mathematical model more accurately.