To address the issues of strong fluctuation of wind power and low prediction accuracy,this paper proposes a hybrid ultra-short-term wind power prediction model that utilizes an improved Dung Beetle Optimizer,namely Logistic-T-Dung Beetle Optimizer (LTDBO),to optimize both the parameters of Variational Mode Decomposition (VMD) and the hyperparameters of Long Short-Term Memory (LSTM) network.Firstly,with the average envelope spectral kurtosis serving as the fitness function,the LTDBO algorithm is employed to optimize the decomposition layers and penalty factors of VMD.Subsequently,the cleaned wind power sequences are decomposed via VMD to obtain the stationary Intrinsic Mode Functions (IMFs) of varying frequencies.Each IMF is then input into the LSTM network,whose hyperparameters have been optimized by LTDBO,for prediction.Finally,the predicted values of all IMFs are superimposed and reconstructed to obtain the final prediction.Experimental results show that the LTDBO algorithm can effectively identify the optimal combination of VMD and LSTM hyperparameters,and the combined model of LTDBO-VMD-LTDBO-LSTM exhibits superior prediction accuracy and robustness in the field of wind power prediction.