The concept and method of multi-objective optimization are applied to the simulated moving bed (SMB) chromatography separation based on multi-objective evolutionary algorithm—non-dominated sorting in genetic algorithms—NSGA-Ⅱ algorithm.The moving bed chromatography separation was simulated using true moving bed (TMB) modeling strategy. Subsequently

using the separation performance parameters as the objective function

two-objective optimization problems were solved to determine the optimal operation conditions.The results showed that the non-inferior solution sets obtained by NSGA-Ⅱ were distributed uniformly and this algorithm had good convergence and robustness.The multi-objective optimization of the optimal operation conditions of the simulated moving bed facilitated the design and operation of a simulated moving bed.