基于Kriging的差分进化算法及其在苯乙烯流程优化中的应用

doi:10.3969/j.issn.0438-1157.2013.12.044

化工学报 ›› 2013, Vol. 64 ›› Issue (12): 4563-4570.DOI: 10.3969/j.issn.0438-1157.2013.12.044

基于Kriging的差分进化算法及其在苯乙烯流程优化中的应用

王晓强, 罗娜, 叶贞成, 钱锋

华东理工大学化工过程先进控制和优化技术教育部重点实验室, 上海 200237

收稿日期:2013-08-14 修回日期:2013-08-29 出版日期:2013-12-05 发布日期:2013-12-05
通讯作者: 叶贞成, 钱锋
作者简介:王晓强（1989- ），男，博士研究生。
基金资助:
国家自然科学基金项目（U1162202，61222303，21206037）；国家高技术研究发展计划项目（2012AA040307）；上海市自然科学基金项目（13ZR1411500）；中央高校基本科研业务费专项基金和上海市重点学科建设项目（B504）。

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

WANG Xiaoqiang, LUO Na, YE Zhencheng, QIAN Feng

Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Received:2013-08-14 Revised:2013-08-29 Online:2013-12-05 Published:2013-12-05
Supported by:
supported by the National Natural Science Foundation of China (U1162202，61222303，21206037)，the High-tech Research and Development Program of China (2012AA040307)，the Natural Science Foundation of Shanghai (13ZR1411500) and the Fundamental Research Funds for the Central Universities and the Leading Academic Discipline Project of Shanghai (B504).

摘要/Abstract

摘要： 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。

关键词: 自适应差分进化算法, Kriging, 苯乙烯流程优化

Abstract: 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.

Key words: self-adaptive differential evolution algorithm, Kriging, styrene plant optimization

中图分类号:

TQ015.9

王晓强, 罗娜, 叶贞成, 钱锋. 基于Kriging的差分进化算法及其在苯乙烯流程优化中的应用[J]. 化工学报, 2013, 64(12): 4563-4570.

WANG Xiaoqiang, LUO Na, YE Zhencheng, QIAN Feng. Differential evolution algorithm based on Kriging and its application in styrene plant optimization[J]. CIESC Journal, 2013, 64(12): 4563-4570.

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基于Kriging的差分进化算法及其在苯乙烯流程优化中的应用

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

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