Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

Multi-objective optimization of polyester mooring system based on particle swam
optimization algorithm
Cong Yi 1, Jiayao Wei2, Shangmao Ai 2, Wangjian Cheng 2
1-Engineering Research and Design Department, CNOOC Research Institute,Beijing, China
2-College of Shipbuilding Engineering, Harbin Engineering University,Harbin, Heilongjiang, China
offshore floating wind turbine mooring system based on the surrogate
model and genetic algorithm. Most of the multi-objective optimization
analysis objects of deep-water mooring system in the industry offloating platforms are offshore power generation platforms and FPSO,
and there are few researches on the optimization design of deep-watersemi-submersible platform mooring system using multi-objectiveoptimization algorithm.

ABSTRACTThe polyester rope has been widely used for the deep-water mooring
system, for the advantages of excellent tensile strength, strong
corrosion resistance, good fatigue property and low cost. To reduce themooring cost and improve the mooring mechanical properties, thispaper combines the approximate model and the multi-objective particle
swarm optimization algorithm to achieve the multi-objective
optimization of the taut polyester mooring system. The polyester rope
volume and strength utilization ratio are defined as the optimization
objectives, then the parameters of mooring radius, the polyester rope
diameter and the length are optimized. The results show that the stressof the mooring line is obviously reduced after optimization, and theheave and sway of the semi-submersible platform are also reduced.
Compared with the other two design parameters, the length of polyesterrope has the most significant effect on the change of strength utilizationrate.

In this paper, quadratic response surface approximation model andmulti-objective particle swarm optimization algorithm are combined tocarry out multi-objective optimization of a semi-submersible platform
mooring system. Both the strength utilization ratio and volume ofpolyester rope are set as the optimization objectives. At first, thesection 1 briefly introduces the polyester mooring analysis tool and themethod of static-dynamic polyester rope stiffness. The mathematicaloptimization model and the whole optimization process are described indetail in section 2. The section 3 presents a mooring example to discuss
the optimization result. The final conclusions are given in the section 4.

KEY WORDS: Multi-objective optimization; Approximate model;taut mooring; Polyester rope volume; Strength utilization rate.

DYNAMIC ANALYSIS BASIS OF POLYESTER ROPEINTRODUCTION

In this study, dynamic analysis of the mooring system of semisubmersible platform was carried out based on the software OrcaFlex.
The mooring line is reduced to the spring-mass system consisting of aseries of mass particles and massless springs. The spring section
simulates only the axial characteristics of mooring line, ignoring
bending and torsion effects. And the mass, weight, buoyancy and othercharacteristics of the mooring line are simulated by nodes. The
Equation of Motion of the mooring system is as follows:

Compared with the chain and the steel rope, the polyester rope material
has the advantages of excellent tensile strength, strong corrosion
resistance, good fatigue property and low cost for the mooring system.
It was noted (Yuan, Incecik and Ji,2014) that polyester rope can reduce
the weight of the mooring system and the mooring radius efficiently. Inaddition, it can also improve the efficiency of the mooring horizontal
recovery force and increase the effective load of the platform.

M ( x, x) + C( x, x) + K ( x) = F ( x, x, t )Li et al (2020) and Bruno et al (2021) analyze the influence of multiple
design parameters on mooring system tension characteristics andfloating platform movement. Sun et al. (2015) optimized the length ofdeep-sea multi-component mooring system based on neural networkand genetic algorithm to minimize the motion response of FPSO. Ajitet al. (2019) simultaneously optimized the cost and fatigue damage of(1)Where F is the external load, K is stiffness load, C is damping load and
M is inertia load, x is the position of the nodes, x is velocity of thenodes, x is acceleration vectors of the nodes and t is the simulation
time. The axial stiffness and damping of the line segment between
nodes are simulated by an axial spring-damper so that the adjacent1947