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Enhancing differential evolution algorithm with a fitness-distance-based selection strategy

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Abstract

Since the introduction of differential evolution (DE) algorithms, they have achieved remarkable success in the field of evolutionary algorithms and engineering applications. In single-objective DE algorithms, most researchers tend to focus on improving mutation operators and parameter control, while overlooking the study of selection operators. However, the study of selection operators still holds great potential in enhancing the performance of DE algorithms. This study proposes a fitness-distance-based selection (FDS) strategy and a new scaling factor control method. FDS is divided into two stages. The first stage is to determine whether an individual needs to accept discarded trial vectors. The second stage involves selectively accepting these discarded trial vectors, which is based on the information related to the discarded trial vectors and the corresponding target vector. A new setting for the scaling factor parameter is proposed, designed to more effectively assist FDS in enhancing algorithm performance. Based on these strategies, an improved variant of the DE algorithm, called fitness-distance-based DE (FDDE) algorithm, is further proposed by this study. To verify the performance of FDDE, we conducted an in-depth study comparing it with six other advanced DE variants and four famous evolutionary algorithms using the CEC 2017, CEC 2022, and CEC 2011 benchmark sets. The experimental results demonstrate that the FDS strategy and the new scaling factor can significantly improve the performance of DE algorithms, and FDDE is significantly better than other advanced algorithm.

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The data used to support the findings of this study are available from the corresponding author upon request.

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Funding

This work was supported by the National Natural Science Foundation of China (62076110) and the Natural Science Foundation of Jiangsu Province (BK20181341).

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Authors and Affiliations

  1. School of Artificial Intelligence and Computer Science, Jiangnan university, Lihu Avenuc, Wuxi, 214122, Jiangsu, China

    Yawei Huang, Xuezhong Qian & Wei Song

Authors
  1. Yawei Huang
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  2. Xuezhong Qian
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  3. Wei Song
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Contributions

Yawei Huang was responsible for methodology and writing—original draft preparation. Yawei Huang, Xuezhong Qian, and Wei Song performed validation. Yawei Huang and Xuezhong Qian contributed to writing—reviewing and editing. Xuezhong Qian and Wei Song were involved in project administration.

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Correspondence to Yawei Huang.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical and informed consent for data used

All data used in this study are derived from publicly available databases. These data are public and do not involve the use of or conflict of interest of personal privacy; therefore, no specific ethical review and informed consent are required. We have adhered to all appropriate provisions and requirements for using these data.

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Appendix

Appendix

See Tables 16, 17, 18 and 19.

Table 16 Mean and Std of algorithms under 10D of CEC 2017
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Table 17 Mean and Std of algorithms under 30D of CEC 2017
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Table 18 Mean and Std of algorithms under 50D of CEC 2017
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Table 19 Mean and Std of algorithms under 100D of CEC 2017
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Huang, Y., Qian, X. & Song, W. Enhancing differential evolution algorithm with a fitness-distance-based selection strategy. J Supercomput 80, 22245–22286 (2024). https://doi.org/10.1007/s11227-024-06298-0

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