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Self-adaptive differential evolution with multiple strategies for dynamic optimization of chemical processes

  • Bin Xu
  • Wushan Cheng
  • Feng Qian
  • Xiuhui Huang
Theory and Applications of Soft Computing Methods
  • 25 Downloads

Abstract

Dynamic optimization has become an increasingly important aspect of chemical processes in the past few decades. To solve such chemical dynamic optimization problems (DOPs) effectively, we put forward a modified differential evolution algorithm named XADE in this paper, which integrates the self-adaptive principle and multiple mutation strategies. In XADE, four mutation strategies with different characteristics are introduced instead of using a single strategy. Meanwhile, the mutation strategies and DE’s two control parameters are gradually adjusted adaptively based on the knowledge learned from the previous searches in generating improved solutions. The advantageous performance of XADE is validated by comparisons with several state-of-the-art adaptive DE variants on 24 complex test instances. Experimental results show that XADE is an effective approach to solving global numerical optimization problems. Moreover, the effectiveness of XADE is validated by applying the approach to 4 real-world complex DOPs with different characteristic in the chemical engineering field.

Keywords

Differential evolution Dynamic optimization Self-adaptive Multiple strategies Chemical processes 

Notes

Acknowledgements

The authors would like to express their sincere thanks to the editors and reviewers for their valuable suggestions and comments. This work was supported by the National Natural Science Foundation of China under Grant 61703268.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Mechanical EngineeringShanghai University of Engineering ScienceShanghaiChina
  2. 2.Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of Science and Technology)Ministry of EducationShanghaiChina
  3. 3.School of Energy and Power EngineeringUniversity of Shanghai for Science and TechnologyShanghaiChina

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