After 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.

This 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.

The 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.

The 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.

In complex mountainous environments, unmanned aerial vehicle (UAV) coverage search tasks often encounter two core challenges: path redundancy and terrain obstructions. Although fixed-pattern search methods offer convenience and high efficiency in simple scenarios, they struggle to effectively avoid dead points and obstructures in complex terrains due to their rigid pre-planned trajectories. As a result, path repetition and reduced search efficiency become particularly prominent. To address the challenges of path redundancy and terrain obstructions in UAV coverage search tasks within complex mountainous environments, this study proposes a hybrid strategy that integrates traditional fixed-pattern search with an improved particle swarm optimization (PSO) algorithm. This strategy optimizes return path planning, minimizes path redundancy, and enhances adaptability in complex terrains.

This research adopts a grid-based modeling approach to discretize complex terrains, constructing a simulation environment using real-world digital elevation model data from a specific area of Luding County, Sichuan Province, China. During data preprocessing, high-precision terrain data are converted into 3D surfaces via bi-linear interpolation, and threshold segmentation algorithms create binary representations of obstacle zones and passable areas. To address the challenge of dead points in fixed-pattern searches, this study introduces a hybrid backtracking mechanism that integrates queue-based and stack-based backtracking. When encountering dead points, an improved PSO algorithm with adaptive inertia weights is introduced to plan safe and efficient cross-regional paths. In the early iterations, the algorithm assigns larger inertia weights to enhance global exploration. Subsequently, these weights are reduced to refine local searches. In addition, path safety is ensured through various constraint functions, including mathematical models to avoid terrain blockages, maintain safe distances from obstacles, and ensure path continuity.

The experimental results indicate that the proposed hybrid strategy exhibits significant advantages in complex mountainous enviornments. This strategy, which combines queue-based backtracking and stack-based backtracking, reduces total path length by 0.66% and 21.1%, respectively. Path coverage gradually increases from initial levels to full coverage (100%), demonstrating robust performance across various terrain conditions. Notably, in highly complex environments, the improved PSO algorithm exhibits faster convergence speed and higher path-planning accuracy than the traditional PSO and the artificial bee colony algorithms. Comparative analysis reveals that stack-based backtracking performs better in complex terrains, whereas queue-based backtracking is more suitable for regions with greater local connectivity. Furthermore, this research is the first to demonstrate that the hybrid strategy can automatically adjust the number of backtrackings without prior information, ensuring flight safety while achieving optimal coverage. The overall optimization reaches 21.1%.

This paper presents a hybrid-strategy-based UAV coverage search method for complex mountainous areas and validates its applicability and superiority across various terrain features through experiments. The findings reveal that the hybrid strategy maintains strong terrain adaptability while balancing efficiency and feasibility. In addition, the selection of backtracking methods directly influences the frequency of heuristic algorithm invocations and ultimately affects the quality of path planning. The successful application of the improved PSO algorithm demonstrates its potential for multi-objective optimization in complex environments, laying a foundation for further exploration of more intelligent and flexible UAV path planning technologies. This study holds significant implications for UAV applications in critical scenarios such as emergency rescue and disaster reconnaissance and provides new perspectives for autonomous UAV navigation.