Abstract: In order to solve the problems of insufficient accuracy and low efficiency in the traditional permanent magnet synchronous motor (PMSM) parameter identification approaches, a parameter identification approach based on multiple improved artificial rabbit optimization (MIARO) algorithm was proposed. Firstly, the initial population distribution was improved by introducing the Fuch mapping hybrid quasi-inverse learning strategy. Secondly, the double-sided mirror reflection mechanism was used to deal with transboundary individuals to ensure the quality and diversity of the population. Finally, in the local development stage of the algorithm, the sine and cosine search and adaptive Cauchy mutation strategy are integrated to help the algorithm jump out of the local optimum. The benchmark function is used to verify the superiority of the MIARO algorithm, and the PMSM identification model established in the d-q axis coordinate system is combined with the MIARO algorithm to realize the PMSM parameter identification. Simulation consequences show that, for PMSM parameter identification multiple improved artificial rabbit optimization algorithm have higher accuracy and efficiency.