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Improved multi-objective differential evolution algorithm based on a decomposition strategy for multi-objective

Mingwei Fan1, Jianhong Chen1, Zuanjia Xie1

  • 1School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou, 510006, China.

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|December 8, 2022
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Summary
This summary is machine-generated.

This study introduces an improved multi-objective differential evolution algorithm (MOEA/D/DEM) for complex engineering tasks. The enhanced algorithm improves population diversity and local search, yielding better results in multi-objective nutrition decisions.

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Area of Science:

  • Engineering Optimization
  • Computational Intelligence

Background:

  • Real-world engineering challenges often involve balancing multiple, conflicting objectives.
  • Multi-objective optimization algorithms provide valuable trade-off solutions for design and exploration.

Purpose of the Study:

  • To propose an improved multi-objective differential evolution algorithm (MOEA/D/DEM) tailored for practical multi-objective nutrition decision problems.
  • To enhance the performance of differential evolution algorithms in handling complex optimization tasks.

Main Methods:

  • A decomposition-based strategy is employed within the differential evolution framework.
  • A neighbor intimacy factor is introduced to improve population diversity.
  • A novel Gaussian mutation strategy with variable step size is developed to enhance local search ability and avoid local optima.

Main Results:

  • The proposed MOEA/D/DEM algorithm demonstrates superior search capability compared to existing multi-objective algorithms.
  • The algorithm achieved competitive results on classic test problems (DTLZ1-7, WFG1-9) and practical nutrition decision problems.

Conclusions:

  • The enhanced MOEA/D/DEM algorithm effectively balances multiple objectives in complex problems.
  • The proposed improvements in diversity and local search contribute to better optimization performance, particularly for nutrition decision-making.