Solving chemical dynamic optimization problems with ranking-based differential evolution algorithms  

作　　者：Xu Chen Wenli Du Feng Qian Xu Chen;Wenli Du;Feng Qian(Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China;School of Electrical and Information Engineering Jiangsu University, Zhenjiang 212013, China)

机构地区：[1]Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China [2]School of Electrical and Information Engineering Jiangsu University, Zhenjiang 212013, China

出　　处：《Chinese Journal of Chemical Engineering》2016年第11期1600-1608,共9页中国化学工程学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China(61333010,61134007and 21276078);“Shu Guang”project of Shanghai Municipal Education Commission,the Research Talents Startup Foundation of Jiangsu University(15JDG139);China Postdoctoral Science Foundation(2016M591783)

摘　　要：Dynamic optimization problems(DOPs) described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-differentiable or explicit mathematical descriptions do not exist. Recently, evolutionary algorithms are gaining popularity for DOPs as they can be used as robust alternatives when the deterministic techniques are invalid. In this article, a technology named ranking-based mutation operator(RMO) is presented to enhance the previous differential evolution(DE) algorithms to solve DOPs using control vector parameterization. In the RMO, better individuals have higher probabilities to produce offspring, which is helpful for the performance enhancement of DE algorithms. Three DE-RMO algorithms are designed by incorporating the RMO. The three DE-RMO algorithms and their three original DE algorithms are applied to solve four constrained DOPs from the literature. Our simulation results indicate that DE-RMO algorithms exhibit better performance than previous non-ranking DE algorithms and other four evolutionary algorithms.Dynamic optimization problems（DOPs） described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-differentiable or explicit mathematical descriptions do not exist. Recently, evolutionary algorithms are gaining popularity for DOPs as they can be used as robust alternatives when the deterministic techniques are invalid. In this article, a technology named ranking-based mutation operator（RMO） is presented to enhance the previous differential evolution（DE） algorithms to solve DOPs using control vector parameterization. In the RMO, better individuals have higher probabilities to produce offspring, which is helpful for the performance enhancement of DE algorithms. Three DE-RMO algorithms are designed by incorporating the RMO. The three DE-RMO algorithms and their three original DE algorithms are applied to solve four constrained DOPs from the literature. Our simulation results indicate that DE-RMO algorithms exhibit better performance than previous non-ranking DE algorithms and other four evolutionary algorithms.

关 键 词：Dynamic optimization Differential evolution Ranking-based mutation operator Control vector parameterization 

分 类 号：O643.1[理学—物理化学] TP18[理学—化学]