Mooring optimization design based on neural network and genetic algorithm

  Objectives  In order to maintain the stability of the position of a ship, a mooring system is required to reduce the translational motion of floating structures.

  Methods  Taking a pipe-laying vessel in the South China Sea as an example, it is possible to minimize the translational displacement of the anchor chain in the mooring state by optimizing the arrangement of the anchor line to ensure the safe operation of the ship. First, we can obtain several different layouts through orthogonal testing after selecting the azimuth and distance of the anchor chain as the test factors. We then calculate the different movements and force in time domain value at different wave direction angles for each layout using Moses. With the calculation results as samples, the BP neural network method achieves time domain simulation in Moses. After choosing the azimuth and distance of the anchor chain as the optimization variables, and with each wave-weighted translational displacement probability as the optimization objective, we find that the generalization capability of the BP neural network method can replace the time domain calculation of Moses.

  Results  Using a genetic algorithm optimization solution, movement is significantly reduced at different wave direction angles.

  Conclusions  This conclusion can provide a reference for the mooring arrangements of floating structures.