Abstract: A discrete whale optimization algorithm (DWOA) is proposed to overcome the defects of low convergence and the lack of ability to solve discrete optimization problems when solving high-dimensional complex problems. In DWOA, the convergence factor is introduced to adjust the distance of the individual from the optimal whale position, the adaptive inertia weight is designed to balance the global exploration and local exploitation ability, the whale optimization algorithm (WOA) is discretized by the improved Sigmoid function. The optimization experiments are conducted on the 9 benchmark functions and oilfield measures planning. Simulation results show that the proposed DWOA has a great improvement in convergence speed and convergence precision.