Firefly algorithm is a new and efficient intelligent algorithm proposed by Dr. Xin-She Yang at Cambridge University. In this paper, the conventional firefly algorithm will be introduced and improved using two-order oscillation. And an improved firefly algorithm based on two-order oscillation is proposed. And this method will be deduced and analyzed for astringency. Six typical functions will be tested to prove performances. Global Max/Min values in six multimodal functions could be found accurately with proposed scheme. The experiment results show our proposed method has excellent performances than conventional one.

The Permanent Magnet Synchronous Motor has been applying widely due to it’s high efficiency, high reliability, relatively low cost and low moment of inertia. However, the PMSM drives are easily affected by the uncertain factors such as the variation of motor parameters and load disturbance. In order to realize the control of the PMSM accurately, a novel adaptive chaotic kinetic molecular theory optimization algorithm was implemented for seeking the best parameters of PID controller. In the PMSM vector control system, the speed loop will be adjusted by a CKMTOA PID controller. In modified kinetic molecular theory optimization algorithm, the adaptive inertia weight factors are used to accelerate the convergence speed, and chaotic searching is conducted within the neighbor set of the solutions to avoid the local minima. The model of PMSM and its` space vector control system are set up in the software of MATLAB/Simulink. The effectiveness of the self-tuning CKMTOA PID controller is verified by comparing with the conventional PID and particle swarm optimization algorithm. The extensive simulations and analysis also show the effectiveness of the proposed approach.