基于自组织映射和K-means聚类的分层设计空间动态缩减方法及其在船型优化中的应用

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

    摘要
  • 摘要:
    目的 基于CFD的船型优化由于其高维、计算昂贵、“黑盒”等特点，通常会存在优化效率低，优化质量差的问题。针对以上问题，基于自组织映射方法和K-means聚类提出分层设计空间动态缩减方法（HSRM）。
    方法 利用K-means聚类算法，对自组织映射方法的可视化结果进行聚类，并提取感兴趣的区域。通过该方式，可在船型优化过程中，对样本仿真数据进行数据挖掘、提取设计知识、指导设计优化，以提高优化质量。最后将该方法应用于7 500吨级散货船的船型优化设计过程以验证有效性。
    结果 结果表明，利用传统粒子群优化算法（PSO）和HSRM得到的优化船型总阻力分别降低1.854%和2.266%，HSRM能得到更高质量的优化解。
    结论 所提出的方法可以指导优化算法向着最优解的方向进行寻优，有效提高优化效率和优化质量。

     

    Abstract:
    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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