面向大规模测控资源调度的自适应动态温感邻域 搜索算法

An adaptive dynamic temperature-aware neighborhood search algorithm is proposed to address challenges in large-scale aerospace TT&C resource scheduling，including dense task conflicts，complex constraints and local optima. The initial solution is generated using a heuristic strategy based on dynamic resource demand. Efficient exploration of the solution space is achieved through adaptive temperature-aware large neighborhood search and multi-level local search. A temperature-aware neighborhood weight domain mechanism enables dynamic switching between weight domains via adaptive temperature regulation. Neighborhood solutions are evaluated using the Metropolis criterion，and selection weights are adjusted dynamically，forming a dual-layer adaptive mechanism combining inter-domain switching and intra-domain optimization. Simulation results demonstrate that the proposed algorithm outperforms adaptive simulated annealing and genetic algorithms in terms of task revenue，convergence speed and solution stability，providing an effective approach for large-scale aerospace TT&C resource scheduling.