Effectively strategizing the trajectories of multiple Unmanned Aerial Vehicles (UAVs) within a dynamic environment to optimize the search for and tracking of mobile targets presents a formidable challenge. In this study, a cooperative coevolution motion-encoded particle swarm optimization algorithm called the CC-MPSO search algorithm is designed to tackle the moving target search issue effectively. Firstly, a Markov process-based target motion model considering the uncertainty of target motion is investigated. Secondly, Bayesian theory is used to formulate the moving target search as an optimization problem where the objective function is defined as maximizing the cumulative probability of detection of the target in finite time. Finally, the problem is solved based on the CC-MPSO algorithm to obtain the optimal search path nodes. The motion encoding mechanism converts the search path nodes into a set of motion paths, which enables more flexible handling of UAV trajectories and improves the efficiency of dynamic path planning. Meanwhile, the cooperative coevolution optimization framework enables collaboration between different UAVs to improve global search performance through multiple swarm information sharing, which helps avoid falling into local optimal solutions. The simulation results show that the CC-MPSO algorithm demonstrates efficacy, reliability, and superior overall performance when compared to the five commonly used swarm intelligence algorithms.

The study of unmanned aerial vehicle (UAV) coverage path planning is of great significance for ensuring maritime situational awareness and monitoring. In response to the problem of maritime multi-region coverage surveillance in complex obstacle environments, this paper proposes a global path planning method capable of simultaneously addressing the multiple traveling salesman problem, coverage path planning problem, and obstacle avoidance problem. Firstly, a multiple traveling salesmen problem–coverage path planning (MTSP-CPP) model with the objective of minimizing the maximum task completion time is constructed. Secondly, a method for calculating obstacle-avoidance path costs based on the Voronoi diagram is proposed, laying the foundation for obtaining the optimal access order. Thirdly, an improved discrete grey wolf optimizer (IDGWO) algorithm integrated with variable neighborhood search (VNS) operations is proposed to perform task assignment for multiple UAVs and achieve workload balancing. Finally, based on dynamic programming, the coverage path points of the area are solved precisely to generate the globally coverage path. Through simulation experiments with scenarios of varying scales, the effectiveness and superiority of the proposed method are validated. The experimental results demonstrate that this method can effectively solve MTSP-CPP in complex obstacle environments.

This study proposes a hierarchical framework with task priority perception for mission planning, to enhance multi-UAV coordination in maritime emergency search and rescue. By establishing a hierarchical decoupling optimization mechanism, the complex multi-region coverage problem is decomposed into two stages: task allocation and path planning. First, a coverage voyage estimation model is constructed based on regional geometric features to provide basic data for subsequent task allocation. Second, an improved multi-objective, multi-population grey wolf optimizer (IM2GWO) is designed to solve the task allocation problem; this integrates adaptive genetic operations and the multi-population coevolutionary mechanism. Finally, a globally optimal coverage path is generated based on the improved dynamic programming (DP). Simulation results indicate that the proposed method effectively reduces total task duration while boosting overall coverage benefits through the aggregation of high-value regions. IM2GWO demonstrates statistically superior performance with respect to the Pareto front distribution index across all test scenarios. Meanwhile, the path planning module based on DP can effectively reduce the overall coverage path cost.