Abstract: To address the problems that the number of iterations for the prediction-based particle swarm optimization (PBPSO) algorithm is difficult to predefine and computational resource utilization is inefficient in multi-region surveillance tasks of MIMO radar in dynamic environments, this paper proposes an adaptive termination method for PBPSO iterative optimization. Firstly, with the effective coverage rate of multiple surveillance regions as the optimization objective, the dynamic multi-objective optimization model for MIMO radar site configuration and the corresponding PBPSO algorithm are elaborated. Then, evaluation metrics characterizing the convergence and diversity of the non-dominated solution set are constructed respectively. Furthermore, to mitigate fluctuations in the convergence degree during iteration, Kalman filtering theory is adopted for smoothing to improve the stability of convergence judgment, and the adaptive stopping criterion of the algorithm is established accordingly. Finally, simulation results verify the superiority of the proposed method over traditional algorithms.