  Wave energy is a fast-developing and promising renewable energy resource. The
primary goal of this research is to maximise the total harnessed power of a
large wave farm consisting of fully-submerged three-tether wave energy
converters (WECs). Energy maximisation for large farms is a challenging search
problem due to the costly calculations of the hydrodynamic interactions between
WECs in a large wave farm and the high dimensionality of the search space. To
address this problem, we propose a new hybrid multi-strategy evolutionary
framework combining smart initialisation, binary population-based evolutionary
algorithm, discrete local search and continuous global optimisation. For
assessing the performance of the proposed hybrid method, we compare it with a
wide variety of state-of-the-art optimisation approaches, including six
continuous evolutionary algorithms, four discrete search techniques and three
hybrid optimisation methods. The results show that the proposed method performs
considerably better in terms of convergence speed and farm output.
