To solve the problem of underdetermined blind source separation (UBSS), an UBSS method based on wavelet packet hybrid optimization is presented. The method, adopting wavelet packet transform, decomposes the observed signal, expands the dimension of the observed signal, and removes redundant signal components with the cross-correlation value, transforming the problem of UBSS. Then, with the singular value decomposition method under the Bayesian information criterion, the number of source signals is estimated, and the signal dimension is reduced through the whitening process. At last, the spiral bubble net hunting behavior and levy flight strategy in the whale optimization algorithm (WOA) are introduced to improve the gray wolf optimization algorithm, and the improved hybrid gray wolf optimization algorithm is integrated with the independent component analysis algorithm to separate the reconstructed positive definite whitening signals, and rewarding separation performance is achieved. The performance of the algorithm is tested by simulation experiments, and the results show the feasibility and effectiveness of the proposed method.