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

Xu Chen; Wenli Du; Feng Qian

doi:10.1016/j.cjche.2016.04.044

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

第25届中国过程控制会议专栏 | Updated：2026-01-06

- Solving chemical dynamic optimization problems with ranking-based differential evolution algorithms
- Solving chemical dynamic optimization problems with ranking-based differential evolution algorithms
- 中国化学工程学报（英文版） 2016年24卷第11期页码：1600-1608
- Affiliations：
  
  2 School of Electrical and Information Engineering, Jiangsu University,Zhenjiang,China,212013
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2016.04.044
  中图分类号：
- 纸质出版：2016
- Accepted：
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Xu Chen, Wenli Du, Feng Qian. Solving chemical dynamic optimization problems with ranking-based differential evolution algorithms[J]. 中国化学工程学报（英文版）, 2016,24(11):1600-1608.

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]. 中国化学工程学报（英文版）, 2016,24(11):1600-1608. DOI： 10.1016/j.cjche.2016.04.044.

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. DOI： 10.1016/j.cjche.2016.04.044.

摘要

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.

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