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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Improved ensemble of differential evolution variants.

Juan Yao1, Zhe Chen2,3, Zhenling Liu4

  • 1College of Informatics, Huazhong Agricultural University, Wuhan, Hubei, China.

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|August 20, 2021
PubMed
Summary
This summary is machine-generated.

This study enhances the Ensemble of Differential Evolution Variants (EDEV) algorithm by revising existing measures and incorporating new techniques. The improved algorithm, Improved Ensemble of Differential Evolution Variants (IEDEV), demonstrates competitive performance against state-of-the-art methods.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • Differential Evolution (DE) algorithms are widely used for optimization.
  • Existing enhancement measures for DE require revision for ensemble applications.
  • Ensemble DE algorithms can be further improved beyond individual constituent performance.

Purpose of the Study:

  • To enhance the Ensemble of Differential Evolution Variants (EDEV) algorithm.
  • To implement revised and novel measures for improved performance.
  • To introduce the Improved Ensemble of Differential Evolution Variants (IEDEV).

Main Methods:

  • Extended the external archive range of JADE within EDEV.
  • Revised and implemented Event-Triggered Impulsive (ETI) control.
  • Applied Linear Population Size Reduction (LPSR) technique.

Main Results:

  • The proposed Improved Ensemble of Differential Evolution Variants (IEDEV) was developed.
  • IEDEV was tested against top-performing algorithms from CEC competitions.
  • Experimental results indicate IEDEV is highly competitive.

Conclusions:

  • The implemented revisions and new measures significantly enhance DE ensemble performance.
  • IEDEV offers a competitive alternative for real-parameter single-objective optimization.
  • Further improvements are possible for ensemble DE algorithms.