In this paper, a multi-objective topology optimization and control scheme based on hyper-heuristic algorithms is proposed to tackle the problems of UAV ad hoc network topology control under complex task scenarios, namely single optimization objective, ambiguous multi-objective priorities, and inadequate fast optimization ability of traditional algorithms. Firstly, a multi-objective joint optimization model covering network connectivity, total throughput, average end-to-end delay, and total communication energy consumption is established, offering a multi-objective collaborative criterion for global network topology performance evaluation. Secondly, a subjective-objective integrated weight calibration method combined with game-theoretic Nash equilibrium is proposed to balance subjective experience and objective data, improving index weighting accuracy across different scenarios. Finally, a hyper-heuristic algorithm integrating the grey wolf optimizer (GWO) and the improved simulated annealing algorithm (ISAA) is designed: The GWO conducts global search to locate high-quality solution regions, while the ISAA’s dynamic cooling strategy accepts suboptimal solutions, realizing global optimality and higher optimization accuracy with fewer iterations. Simulation validations show that the scheme adapts well to different task scenarios and outperforms other advanced heuristic algorithms in convergence speed and global optimization capability.