Optimal design of ship profiles based on bayesian classifier
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 op-timization 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 solu-tion 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 ac-curacy 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.
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