In order to improve the efficiency of multi-parameter, high dimensional and high nonlinear optimization of ship structure reliability optimization design and make up the lack of uncertainty factors affecting structural safety in traditional deterministic optimization design, a river-sea-going ship was taken as the research object. BP (Back Propagation) neural network agent model technique and SMOTE (Synthetic Minority Oversampling Technique) algorithm were used to increase the number of sample points near the failure surface, in order to obtain a high-precision limit state agent model of ship structure with fewer sample points. Combined with Monte Carlo simulation method, the reliability calculation program of hull structure was developed. Structural reliability optimization analysis was performed adopting the simulated annealing optimization algorithm in order to reduce the structural weight. A set of complete and effective reliability optimization design system based on agent model technology was established to improve the efficiency of reliability optimization design, which has guiding significance to the reliability optimization design of river-sea-going ship structures.
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |