Self-adaptive differential evolution algorithm (SaDE) is an improved version of differential evolution (DE) with strategies and parameters changed automatically.SaDE can be used in optimization of chemical process whose model is always computation expensive

and the computation cost can be saved. However

SaDE doesn't perform well when considering convergence rate and precision cause optimization of chemical process is difficult.A Kriging model based SaDE algorithm was proposed to improve performance of SaDE.Using data from SaDE which means the population and objective value measuring searching space during given generations

an approximation model of Kriging can be built. Using gradient of the model

the local optimal point can be obtained which then competes with the best individual of population.By adding the local optimal

the population is perturbed and the whole evolution process is changed if the new local optimal point is better than current optimal individual.This strategy makes the Kriging based SaDE perform better than the original SaDE.Ten benchmark functions from CEC2005 were chosen for testing this new method

and the results showed that Kriging based SaDE performs better than SaDE and DE.Kriging based SaDE was used in optimization of the typical styrene plant which is a computation expensive plant-wide model

and less operation cost was obtained.