[Objective] In response to the challenges associated with the identification of multiple parameters in permanent magnet synchronous motor (PMSM), which are characterized by difficulties and low accuracy, an improved tuna swarm optimization (TSO) algorithm has been proposed. This algorithm is designed to simultaneously identify several parameters of the PMSM. [Methods] Firstly, the Latin hypercube sampling (LHS) method was employed to initialize the tuna population, effectively circumvented the issue of initial aggregation of the tuna population caused by random initialization. This approach enhanced the diversity and uniformity of the initial population. Subsequently, to enhance the algorithm’s optimization performance across different stages of iteration, a dynamic nonlinear weight adjustment strategy was adopted. This strategy equipped the algorithm with stronger global search capabilities during the early stages of iteration, allowed for a broader exploration of the search space, and stronger local search capabilities during later stages, facilitated precise convergence to the optimal solution. Finally, a Gaussian mutation strategy was implemented, enabling the algorithm to effectively explore new solution spaces during the optimization process. This strategy mitigated the tendency of the algorithm to become trapped in local optima, thereby improved the convergence accuracy of the TSO algorithm. [Results] To validate the effectiveness of the proposed method, simulations and experimental analyses were conducted on both simulation software and motor platform. The results of both simulation and experiment demonstrated that the improved TSO algorithm, which incorporated LHS, dynamic nonlinear weight adjustment, and Gaussian mutation, outperforms particle swarm optimization algorithms and the original TSO algorithm in the identification process of PMSM resistance, inductance, and magnetic flux linkage. It achieved faster convergence speeds and higher convergence accuracy, with all parameter identification errors controlled within a range of 0.89%. [Conclusion] The improved TSO algorithm is capable of effectively synchronizing the identification of resistance, inductance, and magnetic flux linkage in PMSM. It exhibits superior identification accuracy and favorable convergence characteristics.