Mooring System Design Optimization Using a Surrogate Assisted Multi-Objective Genetic Algorithm

Abstract

This article presents a novel framework for the multi-objective optimization of o shore re- newable energy mooring systems using a random forest based surrogate model coupled to a genetic algorithm. This framework is demonstrated for the optimization of the mooring system for a oating o shore wind turbine highlighting how this approach can aid in the strategic design decision making for real-world problems faced by the o shore renewable energy sector. This framework utilizes validated numerical models of the mooring system to train a surrogate model, which leads to a computationally e cient optimization routine, allowing the search space to be more thoroughly searched. Minimizing both the cost and cumulative fatigue damage of the mooring system, this framework presents a range of op- timal solutions characterizing how design changes impact the trade-o between these two competing objectives.