To address the challenges of strong coupling between resource allocation and collaborative scheduling in vehicle–UAV cooperative inspections of power distribution lines, as well as the difficulty in balancing efficiency and stability, this paper proposes a path-scheduling feedback-based evolutionary cooperative optimization method. First, an integrated modeling framework for resource allocation and execution scheduling is constructed, incorporating vehicle path decisions and drone task scheduling into a unified optimization space. Next, a feedback-driven two-layer multi-objective evolutionary collaborative optimization algorithm (FB-MOC2) is introduced. The outer layer performs evolutionary search for adaptive resource allocation, while the inner layer solves path planning and collaborative scheduling, with dynamic resource adjustments achieved through execution-layer feedback, forming a data-driven adaptive optimization process. Subsequently, sensitivity analysis is conducted on resource deployment mechanisms, revealing phased evolutionary patterns between resource scale and system performance, and identifying the effective operational range for resource allocation. Finally, the algorithm’s robustness is validated under multiple failure scenarios. Simulation results demonstrate that the proposed method reduces total operation time from 412 min to 315 min, improves battery utilization to 78.5%, and maintains recovery costs within 1.65 times the baseline even under high drone failure rates, while ensuring full inspection coverage. This approach provides an effective bio-inspired and data-driven solution for adaptive resource allocation and robust scheduling in intelligent power distribution line inspections.
Wu et al. (Wed,) studied this question.