  Renewable energy, such as ocean wave energy, plays a pivotal role in
addressing the tremendous growth of global energy demand. It is expected that
wave energy will be one of the fastest-growing energy resources in the next
decade, offering an enormous potential source of sustainable energy. This
research investigates the placement optimization of oscillating buoy-type wave
energy converters (WEC). The design of a wave farm consisting of an array of
fully submerged three-tether buoys is evaluated. In a wave farm, buoy positions
have a notable impact on the farm's output. Optimizing the buoy positions is a
challenging research problem because of very complex interactions (constructive
and destructive) between buoys. The main purpose of this research is maximizing
the power output of the farm through the placement of buoys in a
size-constrained environment. This paper proposes a new hybrid approach of the
heuristic local search combined with a numerical optimization method that
utilizes a knowledge-based surrogate power model.
  We compare the proposed hybrid method with other state-of-the-art search
methods in five different wave scenarios -- one simplified irregular wave model
and four real wave climates. Our method considerably outperforms all previous
heuristic methods in terms of both quality of achieved solutions and the
convergence-rate of search in all tested wave regimes.
