基于Kriging的差分进化算法及其在苯乙烯流程优化中的应用

自适应差分进化算法基于个体生成策略和控制参数自适应，无须人为设置参数，对问题有较好的适应性，但其收敛速度和精度有待提高。将具有较高预测精度的Kriging模型应用于自适应差分进化算法中，建立跟随种群变化的Kriging模型，通过模型极值点与种群最优个体竞争，对种群产生扰动，影响种群进化过程，改善算法的收敛速度和寻优性能。对10个典型测试函数的测试结果表明，该算法较标准和自适应差分进化算法收敛速度加快，收敛精度提高，且具有更好的稳定性。将基于Kriging的差分进化算法应用于苯乙烯装置的流程优化，操作运行费用显著降低。

Differential evolution algorithm based on Kriging and its application in styrene plant optimization

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.