The surging need for public security risk mitigation has placed stricter demands on the modernization of emergency response capacities. Unmanned aircraft systems (UASs) offer a promising solution for liquid anti-riot agent dispersion, yet the complex interaction between rotor-induced downwash and droplet trajectories makes nozzle layout optimization a significant challenge. To address the prohibitive computational costs of traditional Computational Fluid Dynamics (CFD) and the limitations of single-objective optimization, this study proposes an integrated “simulation–modeling–optimization–decision” framework. First, a linear nozzle layout was identified as superior to the traditional circular arrangement, achieving a 44.8% increase in deposition rate. Subsequently, Optimal Latin Hypercube Sampling (OLHS) and CFD simulations were combined to construct high-precision Kriging surrogate models for three key indicators: deposition rate, uniformity, and coverage rate. The NSGA-II algorithm was then employed to solve the multi-objective trade-off, followed by the entropy-weighted TOPSIS method to identify the optimal engineering solution. Results indicate that nozzle count is the dominant system-level variable under the constant per-nozzle flow-rate condition, showing strong positive correlations with all performance indicators. The identified optimal configuration (6 nozzles with a 1.88 m boom length) achieved a 66.1% increase in deposition rate and an 18.7% increase in coverage rate compared to the original circular layout. Furthermore, the surrogate-based framework improved optimization efficiency to 296% compared to full factorial methods. This study provides a scientific theoretical basis and a highly efficient technical pathway for the structural design of high-performance UAV spray systems.

In objective-weighted multi-criteria decision-making (MCDM), a deletion-effect weight is not automatically identifiable when the normalization scale and its reference boundary are determined by the same candidate set. We develop a conditional, auditable framework for fixed-candidate decision matrices with benefit and cost criteria. Log-Ratio Composite Normalization (LRCN) maps each direction-aligned positive value to its log ratio relative to a declared criterion boundary, preserving within-criterion ratio gaps. LRCN-conditioned Deletion-Effect Weighting (LRCN-DEW) assigns each criterion a normalized weight proportional to the change in equal-weight row score under leave-one-criterion-out recalculation. The framework makes the structural, resolution, and candidate-set conditions for interpreting these effects explicit. Deterministic identities, controlled perturbations, ten public matrices, and semi-synthetic generators examine numerical consistency, boundary behavior, module coupling, and recovery. The frozen-boundary coordinate preserves the audited pair gap ($1.000$ versus $0.009$ after re-estimating a min–max boundary). The proposed LRCN–LRCN-DEW configuration is favored under the declared ratio-scale generator, with recovery and Kendall’s agreement of $0.9332$ and $0.9716$, respectively, but other configurations lead under additive noise and in some public cases. The public matrices are used to examine numerical portability; their criterion-level ratio semantics and native measurement resolution were not verified. These findings show that the method provides boundary-consistent and reproducible weighting, while its comparative advantage depends on the measurement and perturbation mechanism.

Airborne capture net devices mounted on unmanned aerial vehicles (UAVs) provide an effective physical countermeasure against rogue drones. However, conventional deterministic designs neglect parameter uncertainties such as manufacturing tolerances and assembly errors that degrade flexible net deployment dynamics. To resolve this, this study proposes a multi-objective robust design optimization framework integrating a feedforward neural network (FNN) with uncertainty quantification. A lumped-mass dynamic model is formulated; static deployment experiments validate its high fidelity during the initial phase (t ≤ 0.08 s), while subsequent full-deployment and contraction phases are captured via experimentally calibrated numerical extrapolation. To mitigate computational costs, an adaptive FNN surrogate model is developed, reducing single-evaluation times from hours to seconds. Monte Carlo simulations coupled with the non-dominated sorting genetic algorithm II (NSGA-II) are then employed to optimize the mean and standard deviation of deployment and effective distances. Compared with deterministic optimization, the robust solution yields a 23.8% increase in the 95th percentile of deployment distance with only a 1.5% decrease in effective distance. Crucially, the standard deviations of these metrics decrease by 22.5% and 38.5%, respectively, demonstrating significantly attenuated sensitivity to parameter variations and offering a valuable engineering framework for robust airborne interception.