Many-objective evolutionary optimization can be viewed as a population-based learning process, yet learning effective search behaviors becomes increasingly difficult as the number of objectives grows due to weakened selection pressure, severe crowding, and limited computational resources. To address this challenge, this pa per proposes a learning-driven coevolutionary framework with adaptive computational resource allocation for many-objective optimization. A high-dimensional projection-based coordinate mapping mechanism is introduced to obtain compact learning representations of the Pareto front distribution. Based on this representation, a crowd ing entropy indicator is developed to capture both local density and global distribution characteristics, enabling population-level structural learning. Leveraging the learned information, a parameter-free computational re source scheduling strategy is designed to adaptively allocate search effort to more informative regions in the objective space, achieving an effective balance between exploration and exploitation. Experimental results on benchmark many-objective problems demonstrate that the proposed approach consistently improves convergence and diversity under limited computational budgets compared with several state-of-the-art algorithms.
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