## 1 Introduction  
Currently, unmanned aerial vehicles (UAVs) are trending towards intelligence, high efficiency, high accuracy, stability, and flexibility, which can already be widely applied in important fields such as search, rescue, mapping, and surveillance [1]. However, UAVs are susceptible to complex terrains such as mountains and buildings when performing missions. Therefore, path planning technology has been one of the main essential aspects in autonomous navigation and has developed into a major research hotspot in the field for UAVs in recent years [2]. Path planning means that the robot (such as UAV, mobile robot, and underwater autonomous vehicle) plans a complete path from the starting location to the target location safely based on the available information and satisfies the various indicators (such as terrain constraint, threat constraint, energy consumption, and path length). Its goal is to find an optimal path with the least costs [3]. Generally, path planning can be primarily divided into three categories, namely: global path planning [4], local path planning [5], and hybrid planning [6], depending on the degree of environmental awareness. Global path planning is an offline method, considering the entire workspace before the path planning process, while local path planning is an online method, considering a small area around the robot [7]. In addition, the algorithm is key to solving the path planning problem, and the performance of the path planning algorithm directly affects the results. Currently, the frequently utilized path planning algorithms can be classified into two categories: traditional algorithms and intelligent algorithms [8]. Moreover, the traditional algorithm consists of four main groups: graph search (e.g., A* algorithm and Dijkstra's algorithm), sampling based (e.g., rapidly exploring random tree), interpolating curve (e.g., line and circle), and reaction based (e.g., artificial potential field) [9]. Because traditional algorithms suffer from poor optimization and high time complexity, intelligent algorithms are increasingly becoming the mainstream algorithms when dealing with path planning problems under complex environmental information. Intelligent algorithms commonly used to solve path planning problems can contain two main categories, one is machine learning containing neural networks [10], reinforcement learning [11], and so on. The other category is meta-heuristic algorithms such as Ant Lion Optimizer (ALO) [12], Whale Optimization Algorithm (WOA) [13], Harris Hawk Optimizer (HHO) [14], Grey Wolf Optimizer (GWO) [15], Salp Swarm Algorithm (SSA) [16], etc. Due to the drawbacks of traditional algorithms such as high time complexity and general optimization results, an increasing number of scholars began to focus on the research of meta-heuristic algorithms and apply these algorithms to guide the path planning of UAVs [1]. Meta-heuristic algorithms originate from simulating biological interaction behaviors or physical phenomena, which are a sequence of approximate optimization algorithms [17; 18; 19]. Compared with traditional algorithms, they are superior in handling complex optimization problems. However, the most basic meta-heuristic algorithm still has the disadvantages of easily falling into local optimum, slow convergence speed, and poor convergence accuracy when solving some complex optimization problems [20]. Consequently, the fundamental meta-heuristic algorithm has been improved by numerous scholars and applied to path planning problems. In order to solve the UAV path planning problem in the 3D flight environment with obstacles, [PERSON] et al. [21] proposed a Hybrid Differential Symbiotic Organisms Search (HD-SOS) Algorithm and introduced the concept of traction function and perturbation strategy in the algorithm to improve the efficiency and robustness of the proposed algorithm, respectively. [PERSON] et al.