Multiobjective Optimization for Dynamic Umbilical Installation Using Non-Dominated Sorting Genetic Algorithm

This paper presents a method of multiobjective optimization based on approximation model for dynamic umbilical installation. The optimization aims to find out the most cost effective size, quantity and location of buoyancy modules for umbilical installation. Due to the highly geometrically nonlinearity and highly responsive dynamic nature in deepwater, dynamic umbilical analysis is very complex and time-consuming. Approximation Model constructed by design of experiment (DOE) sampling is utilized to solve this problem. Non-linear dynamic analyses considering environmental loadings are executed on these sample points from DOE. Non-dominated Sorting Genetic Algorithm (NSGA-II) is employed to obtain the Pareto solution set through an evolutionary optimization process. The optimization results indicate this optimization strategy with approximation model is valid, and provide the optimal deployment way of buoyancy modules.