Array Optimization of Wave Energy Converters via Improved Honey Badger Algorithm

In order to enhance the generation efficiency of wave energy converter (WEC) arrays, an optimization method for three-tether WEC array based on an improved honey badger algorithm is proposed. First, to overcome the shortcomings of the primal honey badger algorithm (HBA), such as slow convergence speed and low convergence accuracy, three improvement strategies are introduced, i.e., good point set initialization, chaos mechanism, and honey badger population mutation. Then, three wave farms including 2-buoy, 10-buoy, and 20-buoy are tested to verify the advancement and effectiveness of the improved honey badger algorithm (IHBA). The simulation results of the 2-buoy array demonstrate that there are multiple groups of optimal solutions in WEC array optimization. Furthermore, IHBA, HBA, genetic algorithm, and particle swarm optimization can find these optimal solutions at different speeds. Nevertheless, with increasing size of the WEC array, three comparative algorithms fall into local optima solutions. On the contrary, IHBA still exhibits a strong optimization ability and can seek global optima solutions. Finally, the q-factor values obtained by IHBA in 10-buoy and 20-buoy arrays reach 1.059 and 0.968, respectively, which are dramatically larger than those of other algorithms.