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.