The search for Multi-UAV cooperative targets in complex environments remains challenging because the joint action space increases rapidly with swarm size, while sensor observations are constrained by limited range, field of view, and obstacle-induced sight occlusion. To address the common limitations of search redundancy and local-optimal traps in existing methods, this study proposes an integrated multi-UAV active cooperative search framework driven by EIG-TS. The framework is underpinned by the symbiotic synergy between macro-space decoupling and micro-heuristic exploration. Specifically, a dynamic Voronoi partitioning mechanism utilizes real-time swarm positions to mathematically decouple the high dimensional joint action space into subspaces, eliminating cooperative search redundancy at the macro level. Within each dynamically assigned region, the proposed EIG-TS strategy combines expected entropy reduction, Beta Thompson sampling, and distance-cost scoring to balance exploitation, uncertainty driven exploration, and flight cost control, while suppressing micro level local-optimal behavior. Simulation results demonstrate that the proposed integrated strategy achieves a target detection success rate of 96.67% and reduces invalid flight distance by 89.1% compared with conventional baselines. Ablation experiments verify that the active exploration engine and the partitioning mechanism improve the final uncertainty accuracy by 33.7%, demonstrating superior environmental adaptability and improving the robustness of multi-UAV search.
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