Research article

Differential evolution with quasi-reflection-based mutation

  • Received: 03 February 2021 Accepted: 09 March 2021 Published: 12 March 2021
  • Differential evolution (DE) is one of the most successful evolutionary algorithms. However, the performance of DE is significantly influenced by its mutation strategies. Generally, different mutation strategies may obtain different search directions. The improper search direction misleads the search and results in the poor performance of DE. Therefore, it is vital to consider the search direction when designing new mutation strategies. Based on this consideration, in this paper, the quasi-reflection-based mutation is proposed to enhance the performance of DE. The quasi-reflection-based mutation is able to provide the promising search direction to guide the search. To extensively evaluate the performance of our approach, $ 30 $ benchmark functions are chosen as the test suite. Combined with SHADE, Re-SHADE is presented. Compared with different advanced DE methods, Re-SHADE can obtain better results in terms of the accuracy and the convergence rate. Additionally, further experiments on the CEC2013 test suite also confirm the effectiveness of the proposed method.

    Citation: Wei Li, Wenyin Gong. Differential evolution with quasi-reflection-based mutation[J]. Mathematical Biosciences and Engineering, 2021, 18(3): 2425-2441. doi: 10.3934/mbe.2021123

    Related Papers:

  • Differential evolution (DE) is one of the most successful evolutionary algorithms. However, the performance of DE is significantly influenced by its mutation strategies. Generally, different mutation strategies may obtain different search directions. The improper search direction misleads the search and results in the poor performance of DE. Therefore, it is vital to consider the search direction when designing new mutation strategies. Based on this consideration, in this paper, the quasi-reflection-based mutation is proposed to enhance the performance of DE. The quasi-reflection-based mutation is able to provide the promising search direction to guide the search. To extensively evaluate the performance of our approach, $ 30 $ benchmark functions are chosen as the test suite. Combined with SHADE, Re-SHADE is presented. Compared with different advanced DE methods, Re-SHADE can obtain better results in terms of the accuracy and the convergence rate. Additionally, further experiments on the CEC2013 test suite also confirm the effectiveness of the proposed method.



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