Abstract: Active wake control technology mitigates the adverse effects of upstream turbine wakes on downstream turbines by coordinating the operational status of each turbine in a wind farm, thereby enhancing the power generation. To explore the patterns of wake control, 10 wind turbines in tandem are taken as the research object. The Jensen wake model and square summation superposition model are used to calculate the wake velocity distribution, and whale optimization algorithm (WOA) is used as the optimization method for axial induction factor distribution whose results are compared with those of particle swarm optimization algorithm (PSO). The results show that the WOA performs better and converges faster than PSO, resulting in a 5.63% to 42.76% increase in the wind farm power output. The distribution pattern of axial induction factors and the optimization effect on wind farm output power are minimally affected by inflow wind speed but are significantly influenced by the number of turbines and their streamwise spacing.