In order to solve the problems of poor local optimization ability,dependence on the quality of initial population,and easily falling into local optimum caused by the global search of aquila optimizer(AO),a multi-strategy integration AO is proposed.The algorithm utilizes the improved Hooke-jeeves alogrithm to optimize the initialized population quality of the basic aquila optimizer.The simulated annealing probability is introduced to improve the local optimal solution,The adaptive weights improve the efficiency of the global search in the early stage and slow down the local search in the late stage to avoid hovering around the positive solution.Through selecting 12 benchmark test functions for experiments,and the mixed aquila optimizer(MAO)is applied to optimize the wind power prediction model.Experimental results show that for single-peak,multi-peak and fixed-dimension functions,the MAO has fas-ter convergence speed and higher accuracy than comparative functions such as the AO.Simulation experiments are implemented 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 is verified that the MAO improves the feasibility and practica-bility of wind power prediction accuracy and speed.
AOHooke-Jeeves algorithmsimulated annealingadaptive weightswind power forecast
NETL
NSTL
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