A Bayesian Optimization Algorithm for the Optimization of Mooring System Design Using Time-Domain Analysis

Dynamic analysis can consider the complex behavior of mooring systems. However, the relatively long analysis time of the dynamic analysis makes it difficult to use in the design of mooring systems. To tackle this, we present a Bayesian optimization algorithm (BOA) which is well known as fast convergence using a small number of data points. The BOA evaluates design candidates using a probability-based objective function which is updated during the optimization process as more data points are achieved. In a case study, we applied the BOA to improve an initial mooring system that had been designed by human experts. The BOA was also compared with a genetic algorithm (GA) that used a pre-trained surrogate model for fast evaluation. The optimal designs that were determined by both the BOA and GA have a 50% lower maximum tension than the initial design. However, the computation time of the GA needed 20 times more than that of the BOA because of the training time of the surrogate model.

Automated summary

Dynamic analysis is powerful but time-consuming for mooring system design. In this study, we proposed a fast convergence Bayesian optimization algorithm (BOA) that updated the objective function as more data points were obtained. Compared with genetic algorithm (GA), which used a pre-trained surrogate model, BOA achieved a 50% reduction in maximum tension for an initial mooring system. However, GA required 20 times more computation time due to the training of the surrogate model.