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Comput."],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:p>Unmanned Aerial Vehicle (UAV) swarms for cooperative reconnaissance-strike missions have emerged as a pivotal component of modern warfare. However, existing simulation models often struggle to comprehensively characterize complex operational elements. To address this challenge, this study proposes a State-Behavior-Decision Hierarchy (SBDH) model, grounded in an in-depth analysis of swarm cooperative characteristics and the operational mechanism of the Observe-Orient-Decide-Act (OODA) loop. By integrating the strengths of Finite State Machines (FSMs) and Behavior Trees (BTs), the model establishes a tri-tier cooperative architecture comprising command, strike, and reconnaissance units. Through the coupling of state nodes, behavior-command nodes, and decision-transition nodes, it enables the parameterized construction of operational models. Furthermore, this paper introduces a grid tracking-back reconnaissance strategy and a spatial optimal matching strike strategy. The former achieves optimal area partitioning based on the Nondominated Sorting Genetic Algorithm II (NSGA-II) for the efficient search for multiple moving targets, while the latter ensures optimal firepower allocation and precision strikes through a spatial greedy optimization method. Simulation experiments evaluate the impact of key variables, including target motion parameters and UAV swarms operational parameters, on combat effectiveness metrics. 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