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In order to solve the problem that the aquila optimizer algorithm concentrates on global search resulting in slightly poor local optimization ability, relies on the quality of the initial population and is prone to fall into local optimum, a multi-strategy mixed aquila optimizer is proposed.The algorithm uses improved Hooke-jeeves to optimize the initialized population quality of the basic aquila optimizer.The introduction of simulated annealing probability improves the easy to fall into the local optimal solution and adaptive weighting improves the efficiency of the global search in the early stage and slows down the local search in the late stage to avoid hovering around the positive solution.Twelve benchmark test functions are selected for experiments and MAO is applied to wind power prediction model optimization.The experimental results show that for single-peak, multi-peak and fixed-dimension functions,MAO has faster convergence speed and higher accuracy than comparative functions such as AO.Simulation experiments on spring, summer, fall and winter datasets,compared with other models,the prediction accuracy in January and October is improved by 15%,and the prediction curves in April and August are smoother.It confirms the feasibility and practicability of MAO for improving the accuracy and speed of wind power prediction.