A self-adaptive differential evolution algorithm with multiple strategies(SMDE) was proposed to overcome premature or localized optimization of differential evolution(DE) as a result of fixed parameter settings. Based on basic framework of classical DE

the first step in SMDE was to create a candidate set of mutation strategy

scale factor(F) and crossover rate(CR). In the followed searching process

mutation strategy

F and CR for each individual variable in next evolutionary generation were determined self-adaptively from the corresponding candidate set according to knowledge learnt from previous searches

so that proper mutation strategies and control parameters could be set at various evolution stages. Compared to other famous DE variants on optimizing 10 routine standard testing problems

SMDE had better search precision and faster convergence rate. Moreover

study on estimation of uncertain parameters in dynamic process systems of chemical engineering showed that SMDE could effectively solve engineering optimization challenges.