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A novel differential evolution algorithm with multi-population and elites regeneration

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  • Yang Cao
  • Jingzheng Luan

Abstract

Differential Evolution (DE) is widely recognized as a highly effective evolutionary algorithm for global optimization. It has proven its efficacy in tackling diverse problems across various fields and real-world applications. DE boasts several advantages, such as ease of implementation, reliability, speed, and adaptability. However, DE does have certain limitations, such as suboptimal solution exploitation and challenging parameter tuning. To address these challenges, this research paper introduces a novel algorithm called Enhanced Binary JADE (EBJADE), which combines differential evolution with multi-population and elites regeneration. The primary innovation of this paper lies in the introduction of strategy with enhanced exploitation capabilities. This strategy is based on utilizing the sorting of three vectors from the current generation to perturb the target vector. By introducing directional differences, guiding the search towards improved solutions. Additionally, this study adopts a multi-population method with a rewarding subpopulation to dynamically adjust the allocation of two different mutation strategies. Finally, the paper incorporates the sampling concept of elite individuals from the Estimation of Distribution Algorithm (EDA) to regenerate new solutions through the selection process in DE. Experimental results, using the CEC2014 benchmark tests, demonstrate the strong competitiveness and superior performance of the proposed algorithm.

Suggested Citation

  • Yang Cao & Jingzheng Luan, 2024. "A novel differential evolution algorithm with multi-population and elites regeneration," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-25, April.
    • Handle: RePEc:plo:pone00:0302207
    DOI: 10.1371/journal.pone.0302207
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    References listed on IDEAS

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    1. Fuqing Zhao & Zhongshi Shao & Junbiao Wang & Chuck Zhang, 2016. "A hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search for job shop scheduling problems," International Journal of Production Research, Taylor & Francis Journals, vol. 54(4), pages 1039-1060, February.
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    Cited by:

    1. Nikunj Mashru & Ghanshyam G Tejani & Pinank Patel & Mohammad Khishe, 2024. "Optimal truss design with MOHO: A multi-objective optimization perspective," PLOS ONE, Public Library of Science, vol. 19(8), pages 1-37, August.

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