## 1 Introduction  
In recent years, Unmanned Aerial Vehicles (UAVs) have been widely deployed in complex operational environments, such as localized warfare, post-disaster rescue, and mountainous logistics, due to their high maneuverability, cost-effectiveness, and reusability [1, 2]. As a core technology for autonomous UAV missions, path planning algorithms play a critical role in ensuring mission success [3]. However, existing classical path planning methods (e.g., Artificial Potential Field (APF), Particle Swarm Optimization (PSO), enhanced A* algorithm) fail to achieve universally optimal performance for all real-world scenarios due to the inherent complexity, dynamism, and diversity of UAV operating environments [4, 5]. For instance, APF is prone to local minima in cluttered environments; simultaneously, PSO and A* struggle with real-time performance in highly dense obstacle fields [6]. To address these limitations, a viable solution lies in the integration and combination of multiple path planning algorithms that are tailored to specific environmental conditions and optimization objectives [7]. Under complex environmental conditions, the combination of various UAV path planning algorithms allows the planning task to fully leverage the advantages of each algorithm, enabling the efficient generation of low-energy, obstacle-free, and real-time flight paths [8, 9]. This combination is not a fusion of multiple algorithms but a combination of flight strategies and mechanisms that are based on the environment [10]. One algorithm combination method is the traditional multi-algorithm approach, which is based on classical graph search. [PERSON] and others, by analyzing the advantages, disadvantages, and applicable scenarios of online algorithms such as APF and the improved A*, concluded that a combined algorithm framework can overcome the limitations of single algorithms in real time and achieve better performance [11]. [PERSON] proposed an A* algorithm combined with the Dynamic Window Method (DWM) for flight safety in path planning algorithms [12]. [PERSON] and others, based on 3D terrain mapping applications, achieved reasonable building shooting angle coverage via the rapidly exploring random tree (RRT) combined with angle information guidance, optimizing routes and calculation speeds [13]. [PERSON] and others proposed a strategy integrating the expanded A*, circular path optimization, and APF methods, balancing global and dynamic planning scenarios and avoiding local optimal situations. However, none of the verifications were conducted in complex environments [14]. The advantage of such algorithms is that route consumption and solution efficiency are generally superior to a single algorithm, but as the size of the global graph and the scene's complexity increase, the increase in computational load after 3D expansion can affect the real-time performance of the algorithm. Another approach is based on biomimetic intelligence methods for algorithm integration. [PERSON] and others combined various heuristic strategies, such as genetic algorithms and PSO, optimizing multiple objectives, including time and cost, and making the strategies suitable for complex scenarios in post-disaster resource scheduling [15]. [PERSON] et al. enhanced planning efficiency in complex scenarios by integrating PSO with vibration functions and introducing a Reference Point Method (RPM), achieving optimal paths, although the results depend heavily on initial parameter settings [16]. [PERSON] et al. transformed the inertia parameter w and learning parameters c1 and c2 in the PSO algorithm from fixed values to nonlinear values that are related to iteration counts, enabling adaptive parameters and accelerating convergence [17]. [PERSON] et al. divided the population intomultifunctional subgroups and introduced a migration mechanism to increase diversity, effectively alleviating homogenization. However, local search capabilities remain limited, and high computational costs are still incurred [18].