In this paper, a multi-objective topology optimization and control scheme based on hyper-heuristic algorithms is proposed to address the existing issues in the topology control of UAV ad hoc networks under complex task scenarios, including single optimization objective, ambiguous multi-objective priorities, and insufficient fast optimization capability of traditional algorithms. Firstly, a multi-objective joint optimization model encompassing network connectivity, total throughput, average end-to-end delay, and total communication energy consumption is established to provide a multi-objective collaborative criterion for the global performance evaluation of network topology. Secondly, a subjective-objective integrated weight calibration method combined with the game-theoretic Nash equilibrium is developed to reconcile subjective experience and objective data, thereby enhancing the accuracy of index weighting for the scheme in different scenarios. Finally, a hyper-heuristic algorithm integrating grey wolf optimizer (GWO) and improved simulated annealing algorithm (ISAA) is designed. It relies on GWO to locate high-quality solution regions through global search and combines the dynamic cooling strategy of ISAA to accept suboptimal solutions, achieving global optimality and improved optimization accuracy with fewer iterations. Extensive simulation validations demonstrate that the proposed scheme exhibits good adaptability to different task scenarios and shows significant advantages in convergence speed and global optimization capability compared with other advanced heuristic algorithms.