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Fidelity-adaptive evolutionary optimization algorithm for 2D irregular cutting and packing problem

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Abstract

The cutting and packing problems (CPP) widely appear in various industrial fields, such as additive manufacturing (AM) and the fashion industry. The evolutionary optimization (EO) algorithms inspired by biological evolution are popular to solve such combinatorial optimization problems these years. Most of the research focused on the improvement of nesting strategies (NS) and EO algorithms, while the relationship between NSs and evolutionary optimization stages is the neglected crucial point. In this paper, a fidelity-adaptive evolution optimization (FAEO) method is proposed to speed up the optimization process by using different nesting strategies at the appropriate optimization stages. In FAEO methods, two switching methods are designed to convert NSs. The neighbourhood-elite evaluation (NEE) and staged-archive (S-A) methods are developed to accelerate individual internal assessment. The experimental results and relevant analysis of the cases from ESICUP by the combination of genetic algorithm (GA) and skyline-derived NSs prove the effectiveness, rapidity, and industrial value of the FAEO algorithm compared with the benchmark algorithms.

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Data availability

The data that support the findings of this study are openly available at https://www.euro-online.org/websites/esicup/data-sets/#1535972088237-bbcb74e3-b507.

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Acknowledgements

The authors would like to acknowledge Haochen Li for his help in algorithm design.

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Authors and Affiliations

  1. Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Bei**g, 100094, China

    Yizhe Yang, Bingshan Liu, **n Li, Qingfeng Jia, Wenyan Duan & Gong Wang

  2. University of Chinese Academy of Sciences, Huairou, China

    Yizhe Yang

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  1. Yizhe Yang
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  4. Qingfeng Jia
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Correspondence to Yizhe Yang or Bingshan Liu.

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Yang, Y., Liu, B., Li, X. et al. Fidelity-adaptive evolutionary optimization algorithm for 2D irregular cutting and packing problem. J Intell Manuf (2024). https://doi.org/10.1007/s10845-024-02329-y

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