Multi-Objective Optimization Based on Response Surface Methodology and Multi-Objective Particle Swarm Optimization for Pipeline Selection of Replenishment Oiler

Ship pipeline selection, as a crucial component of ship pipeline design, is often a time-consuming process due to its high complexity. In this study, the response surface methodology combined with the multi-objective particle swarm optimization algorithm was used to optimize the fuel pipeline resistance and its space volume, aiming to select the optimal design scheme for an X-type replenishment oiler. Firstly, a one-dimensional pipeline system simulation model of a replenishment oiler was established based on the Flowmaster software (version 4.2_0 2020), and the fueling process was simulated. The simulation results were validated against the experimental results, and good agreements were obtained. Then, the response surface methodology was employed to establish regression models for the pipeline resistance, pipeline space volume, and imbalance degree of branch flows. Finally, multi-objective particle swarm optimization was used to optimize the target and select the optimal virtual solution from the Pareto front. Constrained by the international application standard, the optimal real solution was determined. Compared with the original scheme, the optimized scheme reduced the resistance by 3.57% for the #1 pipeline system and by 3.51% for the #2 pipeline system, respectively, and the space volume of the pipeline system was reduced by 5.72% while ensuring the flow balance.