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

doi:10.1016/j.cjche.2016.04.044

Chin.J.Chem.Eng. ›› 2016, Vol. 24 ›› Issue (11): 1600-1608.DOI: 10.1016/j.cjche.2016.04.044

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

Xu Chen^1,2, Wenli Du¹, Feng Qian¹

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

Received:2015-06-09 Revised:2015-12-02 Online:2016-12-06 Published:2016-11-28
Contact: Wenli Du
Supported by:
Supported by the National Natural Science Foundation of China (61333010, 61134007 and 21276078) and "Shu Guang" project of Shanghai Municipal Education Commission, the Research Talents Startup Foundation of Jiangsu University (15JDG139), and China Postdoctoral Science Foundation (2016M591783).

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

Xu Chen^1,2, Wenli Du¹, Feng Qian¹

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

通讯作者: Wenli Du
基金资助:
Supported by the National Natural Science Foundation of China (61333010, 61134007 and 21276078) and "Shu Guang" project of Shanghai Municipal Education Commission, the Research Talents Startup Foundation of Jiangsu University (15JDG139), and China Postdoctoral Science Foundation (2016M591783).

Abstract

Abstract: 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 alternativeswhen 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.

Key words: Dynamic optimization, Differential evolution, Ranking-based mutation operator, Control vector parameterization

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 alternativeswhen 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

Xu Chen, Wenli Du, Feng Qian. Solving chemical dynamic optimization problems with ranking-based differential evolution algorithms[J]. Chin.J.Chem.Eng., 2016, 24(11): 1600-1608.

Xu Chen, Wenli Du, Feng Qian. Solving chemical dynamic optimization problems with ranking-based differential evolution algorithms[J]. Chinese Journal of Chemical Engineering, 2016, 24(11): 1600-1608.

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

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

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