Large-scale multi-objective optimization problems (LSMOPs), characterized by their high-dimensional decision spaces, pose a formidable challenge to solving multi-objective optimization. This paper proposes LMOHHO, an enhanced Harris Hawks optimizer specifically adapted for LSMOPs. The core of LMOHHO integrates the distinct strengths of competitive swarm optimization (CSO) and Harris Hawks optimization (HHO). The algorithm employs CSO for the global search to maintain a diverse population and uses HHO to accelerate convergence. An adaptive switching strategy dynamically manages the balance between these exploration and exploitation phases. The efficacy of LMOHHO is validated by its strong performance in experiments conducted on standard test problems.