# Adaptive Neuro-Surrogate-Based Optimisation Method for Wave Energy Converters Placement Optimisation

## TL;DR

This paper introduces an adaptive neuro-surrogate optimization method using a neural network model to efficiently optimize wave energy converter placement, significantly reducing computational costs while maintaining competitive power output.

## Contribution

The study presents a novel neuro-surrogate optimization approach combining RNN models with meta-heuristics for wave farm layout optimization, addressing computational challenges.

## Key findings

- The neuro-surrogate method achieves comparable power output to evolutionary algorithms.
- It significantly reduces computational costs in wave farm optimization.
- The approach performs well across multiple real-world wave scenarios.

## Abstract

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.