An enhanced multi-objective, multi-leader group whale optimization algorithm (MO-MLSWOAR) is proposed to address multi-objective posture optimization for six-degree-of-freedom robotic milling. A high-stiffness, smooth-motion, and high-accuracy solution is sought for machining complex workpieces under dynamic constraints. First, a multi-objective model is formulated by jointly considering the stiffness–load angle, stiffness-ellipsoid volume, kinematic dexterity, and trajectory smoothness during milling. Next, a multi-leader update scheme, adaptive velocity regulation, and a hybrid position-update strategy are incorporated to enhance the standard whale optimization algorithm (WOA) and particle swarm optimization (PSO), thereby improving performance on this high-dimensional, nonlinear problem. Finally, the algorithm is evaluated on representative milling trajectories and benchmarked against a genetic algorithm (GA), PSO, and standard WOA. Simulations indicate that MO-MLSWOAR achieves higher convergence accuracy and greater solution-set diversity than the three baseline methods. The resulting postures were further validated in physical milling experiments, which corroborated the practical effectiveness of the proposed approach.
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