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March 19, 2020IEEE Transactions on Evolutionary Computation

A Constrained Multiobjective Evolutionary Algorithm With Detect-and-Escape Strategy

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Authors

QZQingling ZhuQZQingfu ZhangQLQiuzhen Lin

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Overview

This randomized trial demonstrates a new strategy to escape stagnation in multiobjective optimization algorithms, improving search efficiency.

Key Points

  • This research aims to improve the performance of multiobjective evolutionary algorithms by addressing stagnation issues caused by constraints.
  • Developed a detect-and-escape strategy using feasible ratio and change rate of constraint violation.
  • Implemented a decomposition-based constrained MOEA with the proposed strategy.
  • Conducted extensive experiments on benchmark problems to assess algorithm performance.
  • The proposed algorithm outperformed five state-of-the-art constrained evolutionary algorithms.
  • Demonstrated increased efficiency in escaping stagnation states during searches.
  • Showed improved results for multiobjective optimization problems when constraints are present.

Cite This Study

Zhu et al. (2020) studied this question.

synapsesocial.com/papers/69da81390f0ab7a47c8358d8https://doi.org/10.1109/tevc.2020.2981949
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