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Dual-Subpopulation as reciprocal optional external archives for differential evolution.

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Summary

This study introduces a novel archive management scheme for Differential Evolution (DE) algorithms, enhancing their global optimization performance. The proposed method improves upon existing strategies like JADE, particularly benefiting L-SHADE.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Evolutionary Computation

Background:

  • Differential Evolution (DE) is a robust global optimization technique.
  • JADE and its variants utilize the DE/current-to-pbest/1 mutation strategy with an external archive.
  • Current DE algorithms archive eliminated individuals, impacting strategy effectiveness.

Purpose of the Study:

  • To propose a new scheme for managing the optional external archive in DE algorithms.
  • To enhance the performance of JADE and its variants through improved archive management.
  • To evaluate the effectiveness of the proposed scheme against existing DE algorithms.

Main Methods:

  • A novel archive management scheme involving two subpopulations, each treating the other as an archive.
  • Implementation of the scheme within JADE, SHADE, and L-SHADE algorithms.
  • Comparative experimental analysis against established DE algorithms (CoBiDE, MPEDE, EDEV, MLCCDE).

Main Results:

  • The proposed archive management scheme significantly enhances the performance of JADE and its variants.
  • L-SHADE, when integrated with the new scheme, demonstrates superior performance compared to four other DE algorithms.
  • The experimental results validate the effectiveness of the proposed subpopulation-based archive management.

Conclusions:

  • The novel archive management scheme offers a significant improvement for DE algorithms, particularly JADE variants.
  • This approach provides a more effective way to utilize archive information in evolutionary optimization.
  • The enhanced L-SHADE algorithm represents a state-of-the-art solution for global optimization problems.