The Wiener model is a typical block-oriented nonlinear model, which is widely used in the industrial processes. The parameters of the Wiener model can't be estimated directly due to its nonlinear structure. In this paper, the parameters estimation of a Wiener system is transformed into a nonlinear optimization issue. Then, the brain storm optimization (BSO) algorithm is used to get the optimal values on using parallel searching strategy. Furthermore, the convergent velocity and estimated accuracy are improved by using the feedback information in mutation process. A numerical simulation and a case study validate the effectiveness of the proposed algorithm.