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Wind is a promising source of renewable energy which can be harvested using wind turbines placed on farms. An efficient wind farm layout achieving various engineering and financial objectives is crucial to ensure the sustainability and continuity of energy production. In this study, a high-level search technique, namely late acceptance selection hyper-heuristic is applied to optimise the layout of wind farms. This approach aims to find the best placement of turbines at a given site, maximising the energy output while minimising the cost at the same time. The computational experiments indicate that the late acceptance selection hyper-heuristic improves upon the performance of a previously proposed genetic algorithm across all scenarios and an iterated local search over the majority of scenarios considering the best solutions obtained by each algorithm over the runs.

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