  The installed amount of renewable energy has expanded massively in recent
years. Wave energy, with its high capacity factors has great potential to
complement established sources of solar and wind energy. This study explores
the problem of optimising the layout of advanced, three-tether wave energy
converters in a size-constrained farm in a numerically modelled ocean
environment. Simulating and computing the complicated hydrodynamic interactions
in wave farms can be computationally costly, which limits optimisation methods
to have just a few thousand evaluations. For dealing with this expensive
optimisation problem, an adaptive neuro-surrogate optimisation (ANSO) method is
proposed that consists of a surrogate Recurrent Neural Network (RNN) model
trained with a very limited number of observations. This model is coupled with
a fast meta-heuristic optimiser for adjusting the model's hyper-parameters. The
trained model is applied using a greedy local search with a backtracking
optimisation strategy. For evaluating the performance of the proposed approach,
some of the more popular and successful Evolutionary Algorithms (EAs) are
compared in four real wave scenarios (Sydney, Perth, Adelaide and Tasmania).
Experimental results show that the adaptive neuro model is competitive with
other optimisation methods in terms of total harnessed power output and faster
in terms of total computational costs.
