针对无人机在城市中的物流任务分配问题，提出了一种改进遗传模拟退火算法。首先，考虑到异构无人机的性能限制以建立硬约束，设置任务时间窗口作为软约束，构建总代价最小的无人机任务分配模型。其次，运用自适应交叉变异率改进遗传算法，并采取精英保留策略，使搜索的广度和精度达到动态平衡；为避免遗传算法陷入局部最优，引入模拟退火算法，采用自适应温度衰减函数来提高解的质量。在改进的融合算法中，通过随机概率选择部分子代进行局部优化，动态调整模拟退火的使用频率，以便在全局搜索和局部优化之间找到平衡。仿真实验结果表明，所提出的改进遗传模拟退火算法（improved genetic-simulated annealing algorithm, IGASA）相较于传统遗传算法和模拟退火算法，总代价分别降低了9.5%和7.4%。

An improved genetic-simulated annealing algorithm was proposed for the logistics task allocation problem of UAVs in cities. Firstly, considering the performance limitations of heterogeneous UAVs, hard constraints were established and task time windows were set as soft constraints to construct a drone task allocation model with the minimum total cost. Secondly, the adaptive crossover mutation rate was used to improve the genetic algorithm, and an elite retention strategy was adopted to achieve a dynamic balance between the breadth and accuracy of the search. To avoid genetic algorithm getting stuck in local optima, simulated annealing algorithm was introduced, and an adaptive temperature decay function was used to improve the quality of the solution. In the improved fusion algorithm, partial offspring were selected through random probability for local optimization, and the frequency of simulated annealing was dynamically adjusted to find a balance between global search and local optimization. The simulation experiment results show that the proposed IGASA (improved genetic-simulated annealing algorithm) reduces the total cost by 9.5% and 7.4% respectively compared to traditional genetic algorithms and simulated annealing algorithms.