YU Q, LI P, ZHENG Q, et al. Hierarchical space reduction method based on self-organizing maps and K-means clustering for hull form optimization[J]. Chinese Journal of Ship Research, 2024, 20(6): 1–10 (in Chinese). DOI: 10.19693/j.issn.1673-3185.04050
Citation: YU Q, LI P, ZHENG Q, et al. Hierarchical space reduction method based on self-organizing maps and K-means clustering for hull form optimization[J]. Chinese Journal of Ship Research, 2024, 20(6): 1–10 (in Chinese). DOI: 10.19693/j.issn.1673-3185.04050

Hierarchical space reduction method based on self-organizing maps and K-means clustering for hull form optimization

  • Objective Due to its high-dimensional, computationally expensive, and 'black-box' characteristics, hull form optimization based on CFD usually leads to low efficiency and poor quality. To solve the above problems, this paper proposes a hierarchical space reduction method (HSRM) based on the self-organizing maps method (SOM) and K-means clustering.
    Method The visualization results of SOM are clustered and the regions of interest are extracted. In this way, data mining is used to extract the knowledge implicit in the sample simulation data which can then be used to guide hull form optimization and improve its efficiency and quality. The proposed method is applied to the optimization of a 7 500-ton bulk carrier shape line.
    Results The results show that the total drag of the optimized ship type obtained using traditional particle swarm optimization (PSO) and HSRM is reduced by 1.854% and 2.266% respectively, with HSRM leading to a higher-quality optimized solution.
    Conclusion The proposed method can guide the optimization algorithm to search in the direction of the optimal solution, effectively improving the efficiency and quality of optimization.
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