## 1 Introduction
Reference [16] proposed an improved hybrid ant colony optimization algorithm (JPIACO) based on the jump point search strategy, where the initial pheromone concentration distribution obtained by jump point search can guide the algorithm to find the search path quickly and reduce the inflection points of the path, which effectively improves the parameter sensitivity of the algorithm. However, the performance of this algorithm has not been verified in dynamic environments. All of the above algorithms have been applied to single-UAV target search tasks. Single-UAV systems are usually easy to control, easy to deploy, and suitable for small-scale tasks, but for large-scale and highly dynamic search areas, the search efficiency of single UAVs may be low. Compared with single-UAV target search, multi-UAV collaborative target search has higher search efficiency and wider search scope, and it has gradually become a research hotspot. Ref. [17] models multi-UAV moving target search as a parallel optimization problem and proposes the MEGA-MPC algorithm, which combines the motion encoding mechanism (ME) and the multi-parent genetic algorithm (GA-MPC), for the solution. Unfortunately, the method does not consider mutual communication between UAVs, and the search efficiency is low when multiple areas with high probabilities (MPCs) exist at the same time. Reference [18] proposes a distributed robotic PSO (DRPSO) method to improve the exploration capability of clustered robots in dynamic multi-target search in unknown environments. The method can search out both static and dynamic targets simultaneously while considering realistic constraints such as unknown target initialization, different robot position initialization, limited communication, different obstacle layouts, and decentralized cooperation. Ref. [19] proposes a rule-inspired collaborative search algorithm based on multiple ant colony UAVs which utilizes association rules to drive target probability graph updates. The model is characterized by rule linking, which effectively improves the accuracy of target detection probability in unknown environments. Reference [20] proposes a dynamic discrete pigeon heuristic optimization algorithm for the UAV coordinated search and attack mission planning problem, which combines centralized task assignment and distributed path generation. The algorithm is more easily adaptable to complex, dynamic, and uncertain environments due to its dynamic adaptability and heuristic search properties. However, the algorithm needs to maintain the balance of task allocation and path generation simultaneously, which increases the complexity of the algorithm, and the computational expense is large when dealing with large-scale problems. To summarize, while swarm intelligence algorithms offer numerous benefits in addressing dynamic target search issues, they are susceptible to getting trapped in local optimal solutions as the number of target regions grows and the impact of low-probability regions extends. Furthermore, some swarm intelligence algorithms may exhibit limited adaptability to changes in the environment. To effectively pursue a target that frequently changes its location or deviates significantly, the algorithms must enhance their adaptability. Intelligent optimization algorithms based on the Cooperative Coevolution framework usually decompose a complex problem into multiple sub-problems, each of which is evolved by a sub-swarm, while the sub-swarms share and compete with each other through the co-evolutionary mechanism for information sharing [21]. Such algorithms have excellent performance in solving large-scale, high-dimensional optimization problems and have been tested in real-world problems such as control parameter optimization, function approximation, and classification [22; 23]. Learning from the research of co-evolutionary algorithms applied in other fields, this paper proposes a particle swarm optimization algorithm incorporating multiple swarm co-evolutionary and motion coding mechanisms and applies it to a dynamic target search problem.