Multidisciplinary and multi-objective optimization of a deep-sea hyperspectral instrument module based on collaborative optimization

li.The deep-sea hyperspectral imaging system is essential for acquiring high-precision seabed mineral resource data. To improve the design efficiency and structural performance of the imager’s pressure-resistant hull, this study establishes a multidisciplinary collaborative optimization model based on multi-objective principles. Using Python-based secondary development of Abaqus for parametric analysis, optimal Latin hypercube sampling, and response surface modeling, the NSGA-II algorithm yields an optimized design that reduces mass by 2.7%, increases drainage volume by 3.8%, and improves space utilization by 6.1%. Dive tests with the Jiaolong submersible in 2022-2023 validated the feasibility of the proposed method.