Mooring System Design Optimization Using a Surrogate Assisted Multi-Objective Genetic Algorithm
Pillai, AC; Thies, PR; Johanning, L
Date: 16 October 2018
Article
Journal
Engineering Optimization
Publisher
Taylor & Francis
Publisher DOI
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 ...
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.
Engineering
Faculty of Environment, Science and Economy
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