Variable selection for nonlinear soft sensor development with enhanced Binary Differential Evolution algorithm

In this paper, two enhanced Binary Differential Evolution (BDE) algorithms are proposed to select variables for nonlinear process soft sensor development. Firstly, the Parallel BDE (PBDE) algorithm is presented to extract the optimal individuals of several parallel short evolution paths of basic BDE, where the spurious variables are effectively eliminated. And the most relevant variables are selected through a double-layer selection strategy with the validating Root Mean Square Error (RMSE) for evaluating criterion. Secondly, the Boosting BDE (BBDE) algorithm is proposed through applying the boosting technique to the parallel evolution paths. The performance of the previous path needs to be taken into account when conducting the current evolution path. The selected probabilities of variables are given through the weighted summation of the selection results of all paths. Also, a double-layer selection is conducted on BBDE algorithm. The feasibility and effectiveness of the proposed methods are demonstrated through a nonlinear numerical example and a real industrial process.