Accuracy of parameter estimation and efficiency of state simulation are common concerns in the implementation of state-space models. Even widely used methods such as Kalman filters with MCMC and Particle Filter, still present concerns with efficiency and accuracy, despite their successful results in their respective applications.This article presents a new method combining the structure of particle learning and bare bones particle swarm optimization (BBPSO) to the process of smoothing and filtering the states in the state-space models, thus overcoming the efficiency and accuracy problems. Sampling importance re-sampling is used to estimate the states of the model, then the parameters can be estimated via BBPSO, as an alternative to the kernel approximation of Liu and West. Our method is applied to stochastic volatility and AR(1) state-space models. Empirical results with Ibovespa and SP500 index show better performance when compared to particle filters, thus improving efficiency and accuracy.