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Dual-Population Adaptive Differential Evolution Algorithm L-NTADE

Author

Listed:
  • Vladimir Stanovov

    (School of Space and Information Technologies, Siberian Federal University, 660074 Krasnoyarsk, Russia
    Institute of Informatics and Telecommunication, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia)

  • Shakhnaz Akhmedova

    (Independent Researcher, 12489 Berlin, Germany)

  • Eugene Semenkin

    (School of Space and Information Technologies, Siberian Federal University, 660074 Krasnoyarsk, Russia
    Institute of Informatics and Telecommunication, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia)

Abstract

This study proposes a dual-population algorithmic scheme for differential evolution and specific mutation strategy. The first population contains the newest individuals, and is continuously updated, whereas the other keeps the top individuals throughout the whole search process. The proposed mutation strategy combines information from both populations. The proposed L-NTADE algorithm (Linear population size reduction Newest and Top Adaptive Differential Evolution) follows the L-SHADE approach by utilizing its parameter adaptation scheme and linear population size reduction. The L-NTADE is tested on two benchmark sets, namely CEC 2017 and CEC 2022, and demonstrates highly competitive results compared to the state-of-the-art methods. The deeper analysis of the results shows that it displays different properties compared to known DE schemes. The simplicity of L-NTADE coupled with its high efficiency make it a promising approach.

Suggested Citation

  • Vladimir Stanovov & Shakhnaz Akhmedova & Eugene Semenkin, 2022. "Dual-Population Adaptive Differential Evolution Algorithm L-NTADE," Mathematics, MDPI, vol. 10(24), pages 1-20, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4666-:d:998133
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