The rapid implementation of multi-task decoupling in restricted flight areas for unmanned aerial vehicle swarms is crucial to ensure swarm effectiveness. This study introduces a task-switching mechanism in the bio-inspired rule-based (Bio-RB) decision-making algorithm and establishes a mapping relationship from behavioral rules to task modes. A complete decision model is constructed for the cooperative search and package delivery tasks. To further improve the search efficiency of swarms in restricted areas, a boundary-handling strategy based on the combination of path prediction and virtual agents is proposed. The overall scheme is termed the task-driven rule-based (Task-RB) decision-making algorithm. The proposed Task-RB method is evaluated under full-flow simulation. Numerical experiments demonstrate the superior performance of the proposed Task-RB method against the Bio-RB method under different instances.

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