An Adaptive Weighted Multi-Objective Cooperative Scheduling Method for UAV Swarms in Complex Dynamic Scenarios

Existing contract-net-based UAV scheduling methods commonly use static or event-switched objective weights and provide limited evidence on how state-responsive scoring behaves under failures and communication impairment. This paper proposes a Dynamic Weight Multi-Objective Algorithm (DW-MOA) within a manager-mediated contract-net framework. Five candidate-level objectives are converted into normalized benefit utilities and combined using non-negative state-dependent weights. An independent urgency-gated spatial incentive supports cross-region dispatch without introducing negative objective weights. The method is evaluated against Traditional-CNP, Static-MOA, and HCNP-2022-adapted through matched dynamic scenarios, together with ablation, sensitivity, normalization-stability, communication-impairment, and scalability analyses. DW-MOA improves temporal performance and completed-load balance relative to Traditional-CNP, while comparisons with the stronger baselines reveal trade-offs among response time, normalized energy expenditure, flight distance, and load balance rather than uniform superiority. The mechanism remains computationally tractable at the largest tested scale and shows measurable degradation under modeled auction delay and candidate-bid loss. These findings support DW-MOA as an interpretable state-responsive scheduling method for normalized multi-UAV simulations, while physical energy calibration, continuous wireless-channel modeling, and hardware validation remain subjects for future work.