@article{LIU2026, 
author = {Quanyi LIU and Yue WANG and Hongzhou AI and Pei ZHU},
title = {Multi-objective optimization of helicopter-unmanned aerial ehicle cooperative rescue in post-earthquake scenarios},
year = {2026},
journal = {Journal of Tsinghua University (Science and Technology)},
volume = {66},
number = {6},
pages = {1164-1177},
keywords = {post-earthquake scenarios, collaborative rescue, heterogeneous aircraft, multi-objective optimization, rescue task allocation, non-dominated sorting genetic algorithm},
url = {https://www.sciopen.com/article/10.16511/j.cnki.qhdxxb.2026.26.030},
doi = {10.16511/j.cnki.qhdxxb.2026.26.030},
abstract = {ObjectiveAfter an earthquake, helicopters and unmanned aerial vehicles (UAVs) can be effective means of emergency rescue response when roads are destroyed, power outages occur, and communications are disrupted in hilly and mountainous areas. Owing to the different functionalities and uses of UAVs and helicopters, which operate in different airspaces, existing research tends to schedule and optimize these two types of rescue equipment separately. If they do not account for the cooperation between the two heterogeneous aircraft, the overall rescue efficiency is reduced.MethodsThis study aims to optimize helicopter and UAV teams for search and rescue operations in a post-earthquake environment. This study proposes an innovative parallel rescue framework for the simultaneous coordination of heterogeneous aircraft, rather than classical sequential methods. This study focuses on a performance-based hybrid task allocation model that systematically leverages the specificities of different aircraft types while simultaneously considering the satisfaction of the rescue as a key optimization. This optimization goal balances operational benefits with timely mission accomplishment. It is measured by the total distance flown and the overall satisfaction with task completion. The mathematical model dynamically adapts the priority of affected areas. It also includes several operational constraints, such as the number of aircraft, the frequency of service, payload capacity, endurance limits, and time windows for effective rescue response. To overcome this complex, multiobjective optimization issue, this study designed an improved evolutionary algorithm called greedy-enhanced non-dominated sorting genetic algorithm Ⅱ(GE-NSGA-Ⅱ), which was developed based on an improved mutation strategy adapted to population distribution characteristics.ResultsThe algorithm created a mechanism for adjusting the greedy-adaptive intensity and ensured robust randomness during the initial search phase. Over time, as the process evolved, the local search intensity improved. Even with adjustments, the method remained stronger and more robust than others due to improved global search capabilities. Moreover, the derived solutions remained disparate owing to the intensity provided, thus improving the overall process. The experimental results confirmed the effectiveness and superiority of the model using data from the 2008 Wenchuan earthquake. The ordinary NSGA-Ⅱ and improved GE-NSGA-Ⅱ algorithms were compared for optimal scheduling in three different post-earthquake rescue scenarios. In scenario 1, the total flight distance decreased by 23.04%, whereas satisfaction increased by 5.98%, showing the most significant optimization effect. In all three scenarios, with the increased scale of rescue operations and resource allocation complexity, the total flight distance increased by 210.00%, whereas satisfaction decreased from 0.855 1 to 0.611 5. Sensitivity analysis of parameters was based on population size, crossover probability, and mutation probability.ConclusionsThe findings of this study show that the proposed framework ensures that critically injured individuals receive priority search and rescue coverage in disaster scenarios. Moreover, the framework can dynamically adapt to continuously evolving operational requirements. The flexibility of a cooperative system is characterized by the aircraft's ability to allocate tasks according to its performance profile. For example, UAVs can be used effectively for clustered assessment missions in enemy zones, whereas helicopters can perform long-range heavy-lift operations. The results of this in-depth comparison show that the proposed algorithm is better than the traditional optimization algorithms at achieving the quality of the generated task allocation schemes and promoting maximum efficiency in resource utilization and timely rescue. This study provides a scientific decision-support framework for rescue commanders to coordinate the dispatch of heterogeneous aerial assets. The operational efficiency is greatly improved during the crucial golden rescue time. This study has immediate applications in earthquake or disaster response. In addition, it can be useful for more general coordination problems involving complex multiagents arising in other emergency situations that require dynamic allocation of heterogeneous resources, and responses must occur under severe constraints and in a time-critical environment.}
}