Cooperative on-ramp merging control for connected and automated vehicles (CAVs) can significantly enhance traffic flow and fuel efficiency at highway merging points. However, in mixed traffic scenarios where CAVs coexist with human-driven vehicles (HDVs), the unpredictable behavior of HDVs poses challenges to safety and coordination. While many cooperative merging strategies focus on individual CAV control, fewer have addressed the coordination of multiple CAVs in such settings. This study introduces an optimization-based cooperative merging strategy for all CAVs within a control zone, considering interactions with HDVs of uncertain intentions. A key innovation is the strategic influence of CAVs on HDV behavior by slowing down the CAV preceding HDVs, thereby allowing other CAVs on the adjacent road to merge in front of the HDVs with reduced uncertainty. The optimal slowdown pattern is identified by evaluating CAV throughput across various candidate patterns, with dynamic optimization applied at each time a new vehicle enters the control zone to effectively manage HDV uncertainties. Experimental results from various mixed-traffic scenarios show that the proposed strategy reduces the average travel time delay (ATTD) by up to 54% compared to the first-in-first-out (FIFO) benchmark.