## 3 Key Technologies of UAV-Assisted SAGINs
### Path Planning
#### 3.3.3 Hybrid Algorithm-Based Path Planning  
In UAV path planning, a single algorithm often struggles to balance global optimization and real-time responsiveness. As a result, hybrid algorithms have gradually gained widespread attention as an effective solution to complex path planning problems [99]. By integrating heuristic algorithms with machine learning techniques, hybrid algorithms achieve a better balance between global optimality and real-time performance. They exhibit enhanced adaptability and efficiency, particularly when addressing challenges such as dynamic constraints and multi-objective optimization. Consequently, hybrid algorithms have become a critical research direction in UAV path planning.  
Specifically, some studies focus on combining heuristic search and reinforcement learning techniques to achieve superior path planning in complex flight environments. For example, [PERSON] et al. [93] proposed a global-local hybrid UAV path planning algorithm based on the A* algorithm and Q-learning. The algorithm improves the A* algorithm's search strategy, step size, and cost function and simplifies the OPEN set to enhance planning efficiency. Meanwhile, by incorporating a dynamic exploration factor into Q-learning, it addresses the exploration-exploitation trade-off, effectively adapting to local dynamic path adjustments and significantly improving path quality, execution efficiency, and safety.  
Another category of hybrid algorithms integrates bio-inspired intelligent algorithms with deep learning techniques to tackle complex path planning problems. For instance, [PERSON] et al. [94] proposed the DL-GA algorithm, which uses a GA to collect state and path data from diverse scenarios, generating high-quality samples for training deep neural networks. This integration enables the network to quickly produce optimized paths meeting stringent real-time requirements, improving both planning efficiency and adaptability to complex environments.  
Additionally, [PERSON] et al. [95] proposed an RL-based gray wolf optimization algorithm. By incorporating RL mechanisms, individual agents can adaptively select four operations (exploration, exploitation, geometric adjustment, and optimal adjustment) based on accumulated performance. The algorithm also employs cubic B-spline curves to smooth the generated paths, effectively addressing path planning in complex three-dimensional flight environments. [PERSON] et al. [96] developed a reinforcement learning-based multi-strategy cuckoo search algorithm. This algorithm combines large-scale jumps and one-dimensional movements as search strategies, dynamically adjusting parameters during the search process. These enhancements improve the original cuckoo search algorithm's search capability and convergence, significantly boosting performance in solving complex path planning problems. [PERSON] et al. [97] proposed a multi-modal cooperative multi-objective particle swarm optimization algorithm based on RL. This algorithm incorporates three particle update modes--exploration, exploitation, and hybrid--and leverages RL to guide the selection of update strategies. By achieving a balance between global search and local search in complex three-dimensional environments, the algorithm significantly enhances the efficiency and effectiveness of UAV path planning solutions.  
In summary, hybrid algorithms offer greater adaptability and optimization capabilities for UAV path planning by flexibly integrating heuristic algorithms and ML techniques. With ongoing technological advancements, hybrid algorithm-based path planning methods are expected to enable more efficient and safer navigation in increasingly complex flight environments.