Research on the FSW-GWO Algorithm for UAV Swarm Task Scheduling Under Uncertain Information Conditions

In maritime target search missions, UAV swarm task scheduling faces several challenges. These include uncertainties in target states, the high-dimensional multimodal characteristic of the solution space, and dynamic constraints on swarm collaboration. In terms of target position estimation, existing methods ignore the spatiotemporal correlation of target movement. At the level of optimization algorithms, existing algorithms struggle to balance global exploration and local exploitation, and they tend to fall into local optima. To address the above shortcomings, this paper constructs a technical system of “state perception-strategy optimization-collaborative execution”. First, a Serial Memory Iterative Method (GMMIM) integrated with the Gaussian–Markov model is proposed. This method recursively corrects the probability distribution of target positions using historical state data, thereby providing accurate situational support for decision-making. As a result, task scheduling efficiency is improved by 5.36%. Second, the sliding window technique is introduced to improve the Grey Wolf Optimizer (GWO). Based on the convergence of the population’s optimal fitness, the decay rate of the convergence factor is dynamically and adaptively adjusted. This balances the capabilities of global exploration and local exploitation to ensure swarm scheduling efficiency. Simulations demonstrate that the optimization performance of the proposed FSW-GWO algorithm is 16.95% higher than that of the IPSO method. Finally, a dynamic task weight update mechanism is designed. By combining resource load and task timeliness requirements, this mechanism achieves complementary adaptation between swarm resources and tasks.