## 1 Introduction  
Due to their excellent maneuverability and adaptability, along with their ability to carry out diverse tasks in challenging environments, Unmanned Aerial Vehicles (UAVs) are increasingly being utilized in the fields of navigation and localization, target search and rescue, border patrol, agricultural production, and cargo transportation [1, 2, 3, 4]. Conducting coordinated UAV searches in crucial areas is a typical kind of UAV task aimed at enhancing target detection and gathering intelligent information. For such tasks, the traditional approach is based on search theory, and the corresponding search strategy is formulated in advance, so that the target detection probability is maximized [5]. However, for dynamic target search, usually, the specific location information of the target is unknown or only part of the a priori information can be grasped. As the search time passes, the initial position information and environment information of the target will be attenuated to a large extent, and the probability of finding the target will decrease rapidly. Consequently, the search for moving targets utilizing UAVs relies on the starting position information of the target and the UAV's sensor detection capacity. Path planning is then conducted for the UAVs to achieve the highest possible target detection probability within a certain timeframe. The key to solving the dynamic target search problem lies in the development of a path planning algorithm that exhibits efficient search performance. Classical approaches to addressing UAV target search problems such as the Dijkstra method [6], A* method [7], and dynamic programming method [8] ensure the superiority of the final solution but require significant time and resource consumption during processing. In the work of [9], a comprehensive overview of UAV path planning algorithms is provided. The study revealed that the path planning method based on computational intelligence has the capability to achieve higher efficiency, greater flexibility, and enhanced adaptability in complex environments. Swarm-based intelligent algorithms are a subfield of computational intelligence methods, which have excellent performance in modeling collective intelligence and collaborative behavior in nature, making them suitable for use in a wide range of robotics applications. In recent years, swarm-based intelligent target search algorithms have received increasing attention and research (swarm-based intelligent target search algorithm) [10; 11; 12]. A dynamic target search method for UAV based on motion-encoded particle swarm optimization algorithm (MPSO) was first proposed in the literature [13]. The method encodes each search path of a UAV as a set of motion paths, which makes the particles search in motion space instead of Cartesian space. The method is simple, intuitive, and very conducive to engineering implementation. It lays theoretical guidance for the development of subsequent UAV intelligent search path planning techniques. However, the method may lack sufficient adaptability and perform sub-optimally when dealing with complex dynamic search problems. On this basis, reference [14] combines the charged particle optimization algorithm (ECPO) with the motion encoding (ME) mechanism and proposes the ECPO-ME algorithm for solving the UAV moving target search problem. Compared with the MPSO algorithm, the ECPO-ME algorithm has strong global search performance, but the increase in population diversity causes its convergence speed to become slower. Reference [15] proposed an ant colony path search optimization algorithm (ASPV) with visibility for the optimal search path problem (OSPV) for Markovian motion objectives, and the improved ASPV variant has good convergence performance. However, the algorithm usually involves some additional hyperparameters, such as Initial Pheromone Level, Pheromone Update Strength, etc. The selection of these parameters has a great impact on the performance of the algorithm, and it is difficult to find a generalized set of optimal parameters.