To address the low efficiency of traditional search algorithms and the poor optimization performance of heuristic algorithms in multi-UAV cooperative path planning under complex environments, a multi-strategy improved whale optimization algorithm (MSWOA) is proposed. First, a Sine–Cubic hybrid chaotic map is adopted to improve the quality of the initial population. Second, a nonlinear convergence factor is introduced to adaptively regulate the intensity of global exploration and local exploitation, combined with an adaptive spiral coefficient designed to enhance convergence accuracy in later iterations. Finally, a dual-distribution perturbed adaptive differential mutation strategy is utilized to accelerate convergence speed, and a thinking innovation strategy is introduced to prevent the algorithm from falling into local optima. Extensive experiments on twenty-nine benchmark functions from the CEC2017 test suite demonstrate the superior optimization performance of MSWOA. The algorithm is further applied to the cooperative path-planning problem of multiple UAVs in a three-dimensional complex terrain, validating its accuracy and stability.