Multi-Objective Optimization Design for Column Structures of the Semi-Submersible Drilling Platform Using a Hybrid Criteria-Based Parallel EGO Algorithm

Abstract

In engineering design for semi-submersible drilling platforms, it is necessary to improve anti-collision performance by optimizing the platform’s column’ structure. However, collision is usually analyzed through numerical analysis methods such as finite element analysis, which comes with high calculation costs. The genetic algorithm (GA) and other traditional optimization methods require massive numerical simulations, with unacceptable computational complexity. To address the above problems, a parallel efficient global optimization (EGO) multi-objective algorithm, based on hybrid criteria for the Kriging approximate model, is put forward in this paper. The proposed algorithm was validated through six typical multi-objective optimization test functions. The results show that it is superior to classic EGO, in terms of both optimization results and computational efficiency. Lastly, the hybrid criterion-based parallel EGO algorithm proposed in this paper was employed for the anti-collision, lightweight design of the column of the first ice zone semi-submersible drilling platform in China. It was found that the anti-collision capacity of the platform column rose by 11.9% and the structural weight declined by 2.7 t in the optimized design, suggesting obvious optimization effects with respect to the original design.