The optimization of midship sections is characterized by the large amount of design variables and the complex constraints. Most relevant research applied the intelligent optimization algorithm embedded with the rule-based calculation program (e.g., Mars2000) from classification society to deal with this issue, which has a large computation cost. Therefore, a general fast optimization method based on sensitivity ranking is proposed for the optimization of midship sections.

Firstly, the sensitivity of each constraint about each design variable was evaluated. According to the result of sensitivity, the order of design variables to be modified can be obtained when each constraint is violated. Whether the constraint is only related to local variables or not can be determined as well. During optimization iteration, based on the constraint violation of the current scheme, variable adjustment can be made with the above sensitivity information, and the sensitivity result was updated periodically. Finally, minor adjustment of optimized schemes based on coordinate alternation was employed to further improve the optimization effect.

The optimization result of an oil tanker midship section shows that the proposed method can achieve a 5.195% reduction of weight.

Compared with the intelligent optimization algorithm nesting Mars2000 directly, the optimization effect of the proposed method is satisfactory, and the time cost is only 5.58% of the former. The advantage of the proposed method in time cost is quite obvious.

To solve the difficulties of numerous design parameters and time-consuming computation of ship cabin structure optimization, a collaborative optimization method of surrogate model for cabin structure based on sub-model decomposition is proposed.

A grillage was selected at a time, and the sub-model of grillage structure was established based on the finite element model of the current cabin scheme. The surrogate model was constructed for the grillage structure response and optimized based on the sub-model. After the optimization solution of the grillage was obtained, the cabin model was updated, and then the next grillage was optimized. This iteration stopped until one or more rounds of collaborative optimization including all grillages were completed. Finally, a small-scale adjustment of cabin structure size was conducted to obtain the final optimization solution.

The optimization result of a ship cabin structure shows that, compared with the cabin structure optimization method based on the dimensionality reduction surrogate model from the point of view of overall optimization, under the equivalent computational cost, the weight in the optimization result of the proposed method is further reduced by 2.86%, and the structural weight is reduced by 4.96% eventually.

The proposed method has better optimization results and better application value on the structure optimization problem of the high-dimensional ship hull.