【摘要】 气田环境中，由于多个巡检任务并存且任务点之间的距离较远，巡检无人机的调度变得极为复杂且难以高效实施。针对这一现状，为提高调度方案的质量和稳定性，结合精英保留策略对遗传算法进行优化。优化算法采用一种矩阵元素编码方式，这一方法显著减少了计算时间，并确保了后续计算中染色体的完整性。在此编码方式的基础上，计算了每个个体的适应度，并根据适应度高低进行分组，从而提升了计算结果的精准性。进一步，引入了改进式轮盘赌选择法，以确保种群的多样性，极大地降低了算法陷入局部最优解的风险。最后，以川西某气田和鄂尔多斯盆地某气田为背景，进行了多任务点多无人机巡检调度的仿真。仿真结果表明，改进后的遗传算法相比于传统遗传算法和蚁群算法，在计算巡检无人机调度方案时，平均成本分别节约了17.1%和11.3%。更多还原

【Abstract】 In gas field environments, the scheduling of inspection drones becomes highly complex and difficult to implement efficiently due to the coexistence of multiple inspection tasks and the large distances between task points. To address this issue, an improved genetic algorithm was proposed, incorporating an elitist retention strategy. A matrix element encoding method was adopted, which significantly reduced computational time and ensured the integrity of chromosomes in subsequent calculations. After encoding, the fitness of each individual was calculated, and individuals were grouped based on their fitness, thereby enhancing the accuracy of the results. Furthermore, an improved roulette wheel selection method was introduced to ensure the diversity of the population, thereby reducing the risk of the algorithm falling into local optima. Simulation experiments were conducted based on a gas field in western Sichuan and a gas field in the Ordos Basin. The results show that the optimized genetic algorithm saves, on average, 17.1% and 11.3% in cost compared to traditional genetic algorithms and ant colony algorithms, respectively, when calculating the scheduling of inspection drones.更多还原