Journal of Central South University

, Volume 20, Issue 4, pp 950–959 | Cite as

Chemical process dynamic optimization based on hybrid differential evolution algorithm integrated with Alopex

  • Qin-qin Fan (范勤勤)
  • Zhao-min Lü (吕照民)
  • Xue-feng Yan (颜学峰)
  • Mei-jin Guo (郭美锦)
Article
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Abstract

To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. Alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by Alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of Alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of Alopex-DE is the best. Further, Alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained.

Key words

evolutionary computation dynamic optimization differential evolution algorithm Alopex algorithm self-adaptivity 
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Copyright information

© Central South University Press and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Qin-qin Fan (范勤勤)
    • 1
  • Zhao-min Lü (吕照民)
    • 1
  • Xue-feng Yan (颜学峰)
    • 1
  • Mei-jin Guo (郭美锦)
    • 1
  1. 1.Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education (East China University of Science and Technology)ShanghaiChina

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