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Improved ensemble of differential evolution variants

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  • Juan Yao
  • Zhe Chen
  • Zhenling Liu

Abstract

In the field of Differential Evolution (DE), a number of measures have been used to enhance algorithm. However, most of the measures need revision for fitting ensemble of different combinations of DE operators—ensemble DE algorithm. Meanwhile, although ensemble DE algorithm may show better performance than each of its constituent algorithms, there still exists the possibility of further improvement on performance with the help of revised measures. In this paper, we manage to implement measures into Ensemble of Differential Evolution Variants (EDEV). Firstly, we extend the collecting range of optional external archive of JADE—one of the constituent algorithm in EDEV. Then, we revise and implement the Event-Triggered Impulsive (ETI) control. Finally, Linear Population Size Reduction (LPSR) is used by us. Then, we obtain Improved Ensemble of Differential Evolution Variants (IEDEV). In our experiments, good performers in the CEC competitions on real parameter single objective optimization among population-based metaheuristics, state-of-the-art DE algorithms, or up-to-date DE algorithms are involved. Experiments show that our IEDEV is very competitive.

Suggested Citation

  • Juan Yao & Zhe Chen & Zhenling Liu, 2021. "Improved ensemble of differential evolution variants," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-22, August.
    • Handle: RePEc:plo:pone00:0256206
    DOI: 10.1371/journal.pone.0256206
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