船舶结构轻量化设计对于提高船舶的运载能力和实现更大的经济效益具有重要意义，针对传统优化设计方法建立优化模型时约束条件无显式表达的问题，提出基于贝叶斯分类器的船舶型材优化设计方法。首先，依据贝叶斯理论和核密度估计方法构建贝叶斯分类器，然后利用贝叶斯分类器代替隐式约束函数进行求解，最后以T型材的优化设计问题为例进行验证，并将优化结果对比约束条件可显式表达情况下的求解结果进行分析。基于单约束条件的贝叶斯分类器目标函数偏差低于2%，基于多约束条件的贝叶斯分类器求解目标函数偏差在8%左右，且不同的贝叶斯分类器设计方法会对优化求解结果精确程度产生影响。使用贝叶斯分类器做出决策边界能代替实际边界进行优化求解，验证了贝叶斯分类器驱动求解器寻优的可行性，对解决约束条件无显式表达的问题提供了新思路。
The lightweight design of ship structure is important to improve the ship's carrying capacity and achieve greater economic benefits. In response to the problem that the constraints are not expressed explicitly when the traditional optimization design method builds the optimization model, a Bayesian classifier-based optimization design method for ship profiles is proposed. Firstly, a Bayesian classifier is constructed based on Bayesian theory and kernel density estimation method, and then the Bayesian classifier is used to solve the problem instead of implicit constraint function, and finally the optimization design problem of T profile is verified as an example, and the optimization results are compared with the solution results in the case that the constraints can be expressed explicitly. The deviation of the objective function of Bayesian classifier based on single constraint is less than 2%, and the deviation of the objective function of Bayesian classifier solved based on multiple constraints is around 8%, and different Bayesian classifier design methods will have an impact on the accuracy of the optimization solution results. The use of Bayesian classifier to make decision boundary can replace the actual boundary for optimization solution, which verifies the feasibility of Bayesian classifier-driven solver seeking, and provides a new idea to solve the problem of constraint without explicit expression.
2024,46(10): 75-82 收稿日期：2023-06-14
DOI：10.3404/j.issn.1672-7649.2024.10.013
分类号：U663.2
作者简介：柳俊杰(1998-),男,硕士研究生,研究方向为无人艇编队控制
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