Disasters have caused significant losses to humans in the past decades. It is essential to learn about the disaster situation so that rescue works can be conducted as soon as possible. Unmanned aerial vehicle (UAV) is a very useful and effective tool to improve the capacity of disaster situational awareness for responders. In the paper, UAV path planning is modelled as the optimization problem, in which fitness functions include travelling distance and risk of UAV, three constraints involve the height of UAV, angle of UAV, and limited UAV slope. An adaptive selection mutation constrained differential evolution algorithm is put forward to solve the problem. In the proposed algorithm, individuals are selected depending on their fitness values and constraint violations. The better the individual is, the higher the chosen probability it has. These selected individuals are used to make mutation, and the algorithm searches around the best individual among the selected individuals. The well-designed mechanism improves the exploitation and maintains the exploration. The experimental results have indicated that the proposed algorithm is competitive compared with the state-of-art algorithms, which makes it more suitable in the disaster scenario.

在过去的几十年中，灾难给人类造成了重大损失。必须了解灾难情况，以便尽快进行救援工作。无人机（UAV）是一种非常有用和有效的工具，可提高响应者的灾难态势感知能力。本文将无人机路径规划建模为优化问题，其适应度函数包括行进距离和无人机风险，三个约束条件包括无人机高度，无人机角度和有限的无人机坡度。提出了一种自适应选择变异约束的差分进化算法。在提出的算法中，根据其适应度值和约束违例选择个体。个人越好，选择的可能性就越高。这些选定的个体用于进行突变，并且算法在选定的个体中搜索最佳个体。精心设计的机制可以提高开发效率并保持勘探。实验结果表明，与现有算法相比，该算法具有竞争优势，使其更适合于灾难场景。