There’re some issues such as fixed parameters’ value and easy to fall into local optimum in classical PSO algorithm and many improvements. Therefore, an improved PSO algorithm via sampling strategy(SS-PSO) is proposed. Firstly, replacement of the particles’ speed and location via Latin hypercube sampling(LHS) is proposed for speeding up the convergence process. Then, correction of the global best location via random sampling is proposed for fine tuning the global best location. Finally, “double sampling” LHS is proposed for local search to improve the convergence precision. Two new improvements are used to compare with the SS-PSO algorithm. The results show that the SS-PSO algorithm can improve the PSO’s algorithm performance in some extent.