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

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• 作者：Xu Chen¹ ; ² ; Wenli Du¹ ; ^{wldu@ecust.edu.cn} ; Feng Qian¹
• 关键词：Dynamic optimization ; Differential evolution ; Ranking-based mutation operator ; Control vector parameterization
• 刊名：Chinese Journal of Chemical Engineering
• 出版年：2016
• 出版时间：November 2016
• 年：2016
• 卷：24
• 期：11
• 页码：1600-1608
• 全文大小：829 K
• 卷排序：24

文摘

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.