Surrogate-based optimization algorithm in application of design of ship dimensions

The design of ship main dimensions is a complex problem involving multiple disciplines and objectives. Due to the high subjectivity of conventional methods that only optimize through qualitative analysis and manual trial and error, they can’t quantitatively analyze accuracy. Therefore, in this paper, the surrogate model is used for regression analysis of the complex ship principal scale problem, and then the multi-objective evolutionary algorithm is used to optimize the feasible solution generated by the surrogate model. Firstly, the regression model of each optimization objective in the ship main scale problem is established. In order to ensure the accuracy of the Surrogate model, the Root-mean-square deviation and correlation coefficient of the training set and the test set should be taken as the measurement indicators. Then, based on pareto dominance rule, the regression values generated by the surrogate model are selected. And through genetic operations such as mutation and crossover, a large number of new individuals are generated, and the outstanding individuals are screened based on elite retention strategies. Finally, experiments show that the proposed method is superior to the algorithm without surrogate model in accuracy and efficiency.