To address the issues of insufficient local search capability, susceptibility to local optima, and slow convergence in existing Angle of Arrival (AOA) and Time Difference of Arrival (TDOA) joint positioning methods under complex environments, a Hybrid Strategy-Optimized Harris Hawk Optimization (HSHHO) algorithm is proposed. The proposed algorithm constructs a dual-mode cooperative framework consisting of the original and quasi-reflective populations, and employs a bidirectional elite migration strategy to facilitate information exchange and complementary advantages between the two populations. Additionally, during the algorithm"s exploration and exploitation phases, the Golden Sine Optimization and Cauchy Mutation Strategy are respectively integrated to refine the population update mechanism, thereby enhancing the algorithm’s global exploration and local exploitation capabilities. Simulation results demonstrate that, compared with existing algorithms, the HSHHO algorithm exhibits superior performance in terms of convergence speed, global search capability, local refinement, and positioning accuracy.[1]