A Collaborative Optimization Algorithm for Ship Damage Stability Design

Abstract The damage stability of ship is a key performance metric in ship design. The challenge of damage stability optimization is a complex calculation process with multi scene dynamic inflow simulation relying on the cooperation of ship design software such as NAPA. Our work is to establish a collaborative optimization framework to effectively coordinate reinforcement learning (RL) with particle swarm optimization (PSO) and NAPA. The collaboration of RL and PSO realizes the update direction selection of watertight bulkhead position scheme. The collaboration of PSO and Napa promotes the iterative process of watertight bulkhead position and damage stability value A. The experimental results show that compared with the traditional damage stability optimization method, the collaborative optimization method improves the damage stability by 2.36% and reduces the calculation time significantly.