Embodiments of the invention disclose a multi-target-based improved gray wolf optimization algorithm which is used to solve the technical problems that a standard gray wolf algorithm falls into a local optimal value easily and has a low convergence speed and other defects while processing a multi-target optimization problem in the prior art. The method of the embodiments comprises the following steps: S1, setting a wolf pack initialization parameter and a direction correction probability, and randomly initializing wolves' individual positions; S2, calculating an adaptability value of each wolf individual according to a solving target, and selecting the three wolf individuals ranking top; S3, optimizing the wolves' individual positions of a wolf pack, generating moderate wolves, and updating a wolf pack position; S4, executing direction correction operation on the updated wolf pack, controlling the upgraded wolf pack to participate correction of the size of dimensions according to the direction correction probability, and obtaining a corrected wolf pack position; and S5, determining whether an iteration frequency reaches a preset maximum iteration frequency, outputting the corrected wolf pack position as a final optimization result if the iteration frequency reaches the preset maximum iteration frequency, and, if the iteration frequency does not reaches the preset maximum iteration frequency, turning to the S3 so as to continue performing iteration searching.